(all)
Global Humanities | History of Humanities | Liberal Arts | Humanities and Higher Education | Humanities as Research Activity | Humanities Teaching & Curricula | Humanities and the Sciences | Medical Humanities | Public Humanities | Humanities Advocacy | Humanities and Social Groups | Value of Humanities | Humanities and Economic Value | Humanities Funding | Humanities Statistics | Humanities Surveys | "Crisis" of the Humanities
Humanities Organizations: Humanities Councils (U.S.) | Government Agencies | Foundations | Scholarly Associations
Humanities in: Africa | Asia (East) | Asia (South) | Australasia | Europe | Latin America | Middle East | North America: Canada - Mexico - United States | Scandinavia | United Kingdom
(all)
Lists of News Sources | Databases with News Archives | History of Journalism | Journalism Studies | Journalism Statistics | Journalism Organizations | Student Journalism | Data Journalism | Media Frames (analyzing & changing media narratives using "frame theory") | Media Bias | Fake News | Journalism and Minorities | Journalism and Women | Press Freedom | News & Social Media
(all)
Corpus Representativeness
Comparison paradigms for idea of a corpus: Archives as Paradigm | Canons as Paradigm | Editions as Paradigm | Corpus Linguistics as Paradigm
(all)
Artificial Intelligence | Big Data | Data Mining | Data Notebooks (Jupyter Notebooks) | Data Visualization (see also Topic Model Visualizations) | Hierarchical Clustering | Interpretability & Explainability (see also Topic Model Interpretation) | Mapping | Natural Language Processing | Network Analysis | Open Science | Reporting & Documentation Methods | Reproducibility | Sentiment Analysis | Social Media Analysis | Statistical Methods | Text Analysis (see also Topic Modeling) | Text Classification | Wikification | Word Embedding & Vector Semantics
Topic Modeling (all)
Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all)
Distant Reading | Cultural Analytics | | Sociocultural Approaches | Topic Modeling in DH | Non-consumptive Use
Searchable version of bibliography on Zotero site
For WE1S developers: Biblio style guide | Biblio collection form (suggest additions) | WE1S Bibliography Ontology Outline
2133649
Machine Learning
1
chicago-fullnote-bibliography
50
date
desc
year
1
1
1
2846
https://we1s.ucsb.edu/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%222S6R3W3N%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Shadrova%22%2C%22parsedDate%22%3A%222021%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EShadrova%2C%20Anna.%20%26%23x201C%3BTopic%20Models%20Do%20Not%20Model%20Topics%3A%20Epistemological%20Remarks%20and%20Steps%20towards%20Best%20Practices.%26%23x201D%3B%20%3Ci%3EJournal%20of%20Data%20Mining%20%26amp%3B%20Digital%20Humanities%3C%5C%2Fi%3E%202021%20%282021%29.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.46298%5C%2Fjdmdh.7595%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.46298%5C%2Fjdmdh.7595%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D2S6R3W3N%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Topic%20models%20do%20not%20model%20topics%3A%20epistemological%20remarks%20and%20steps%20towards%20best%20practices%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anna%22%2C%22lastName%22%3A%22Shadrova%22%7D%5D%2C%22abstractNote%22%3A%22The%20social%20sciences%20and%20digital%20humanities%20have%20recently%20adopted%20the%20machine%20learning%20technique%20of%20topic%20modeling%20to%20address%20research%20questions%20in%20their%20fields.%20This%20is%20problematic%20in%20a%20number%20of%20ways%2C%20some%20of%20which%20have%20not%20received%20much%20attention%20in%20the%20debate%20yet.%20This%20paper%20adds%20epistemological%20concerns%20centering%20around%20the%20interface%20between%20topic%20modeling%20and%20linguistic%20concepts%20and%20the%20argumentative%20embedding%20of%20evidence%20obtained%20through%20topic%20modeling.%20It%20concludes%20that%20topic%20modeling%20in%20its%20present%20state%20of%20methodological%20integration%20does%20not%20meet%20the%20requirements%20of%20an%20independent%20research%20method.%20It%20operates%20from%20relevantly%20unrealistic%20assumptions%2C%20is%20non-deterministic%2C%20cannot%20effectively%20be%20validated%20against%20a%20reasonable%20number%20of%20competing%20models%2C%20does%20not%20lock%20into%20a%20well-defined%20linguistic%20interface%2C%20and%20does%20not%20scholarly%20model%20topics%20in%20the%20sense%20of%20themes%20or%20content.%20These%20features%20are%20intrinsic%20and%20make%20the%20interpretation%20of%20its%20results%20prone%20to%20apophenia%20%28the%20human%20tendency%20to%20perceive%20random%20sets%20of%20elements%20as%20meaningful%20patterns%29%20and%20confirmation%20bias%20%28the%20human%20tendency%20to%20perceptually%20prefer%20patterns%20that%20are%20in%20alignment%20with%20pre-existing%20biases%29.%20While%20partial%20validation%20of%20the%20statistical%20model%20is%20possible%2C%20a%20conceptual%20validation%20would%20require%20an%20extended%20triangulation%20with%20other%20methods%20and%20human%20ratings%2C%20and%20clarification%20of%20whether%20statistical%20distinctivity%20of%20lexical%20co-occurrence%20correlates%20with%20conceputal%20topics%20in%20any%20reliable%20way.%22%2C%22date%22%3A%222021%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.46298%5C%2Fjdmdh.7595%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fjdmdh.episciences.org%5C%2F8608%5C%2Fpdf%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222022-01-04T21%3A14%3A57Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20interpretation%22%7D%2C%7B%22tag%22%3A%22Topic%20modeling%22%7D%5D%7D%7D%2C%7B%22key%22%3A%2249QSYPXT%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Smith%20and%20Cordes%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESmith%2C%20Gary%2C%20and%20Jay%20Cordes.%20%3Ci%3EThe%20Phantom%20Pattern%20Problem%3A%20The%20Mirage%20of%20Big%20Data%3C%5C%2Fi%3E.%20First%20edition.%20Oxford%26%23x202F%3B%3B%20New%20York%2C%20NY%3A%20Oxford%20University%20Press%2C%202020.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D49QSYPXT%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22The%20phantom%20pattern%20problem%3A%20the%20mirage%20of%20big%20data%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gary%22%2C%22lastName%22%3A%22Smith%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jay%22%2C%22lastName%22%3A%22Cordes%22%7D%5D%2C%22abstractNote%22%3A%22Pattern-recognition%20prowess%20served%20our%20ancestors%20well%2C%20but%20today%20we%20are%20confronted%20by%20a%20deluge%20of%20data%20that%20is%20far%20more%20abstract%2C%20complicated%2C%20and%20difficult%20to%20interpret.%20The%20number%20of%20possible%20patterns%20that%20can%20be%20identified%20relative%20to%20the%20number%20that%20are%20genuinely%20useful%20has%20grown%20exponentially%20-%20which%20means%20that%20the%20chances%20that%20a%20discovered%20pattern%20is%20useful%20is%20rapidly%20approaching%20zero.%5Cn%5CnPatterns%20in%20data%20are%20often%20used%20as%20evidence%2C%20but%20how%20can%20you%20tell%20if%20that%20evidence%20is%20worth%20believing%3F%20We%20are%20hard-wired%20to%20notice%20patterns%20and%20to%20think%20that%20the%20patterns%20we%20notice%20are%20meaningful.%20Streaks%2C%20clusters%2C%20and%20correlations%20are%20the%20norm%2C%20not%20the%20exception.%20Our%20challenge%20is%20to%20overcome%20our%20inherited%20inclination%20to%20think%20that%20all%20patterns%20are%20significant%2C%20as%20in%20this%20age%20of%20Big%20Data%20patterns%20are%20inevitable%20and%20usually%20coincidental.%5Cn%5CnThrough%20countless%20examples%2C%20The%20Phantom%20Pattern%20Problem%20is%20an%20engaging%20read%20that%20helps%20us%20avoid%20being%20duped%20by%20data%2C%20tricked%20into%20worthless%20investing%20strategies%2C%20or%20scared%20out%20of%20getting%20vaccinations.%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22978-0-19-886416-5%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222021-07-01T06%3A42%3A08Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%224IVIKSUK%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Heaven%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EHeaven%2C%20Will%20Douglass.%20%26%23x201C%3BAI%20Is%20Wrestling%20with%20a%20Replication%20Crisis.%26%23x201D%3B%20%3Ci%3EMIT%20Technology%20Review%3C%5C%2Fi%3E%2C%202020.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.technologyreview.com%5C%2F2020%5C%2F11%5C%2F12%5C%2F1011944%5C%2Fartificial-intelligence-replication-crisis-science-big-tech-google-deepmind-facebook-openai%5C%2F%27%3Ehttps%3A%5C%2F%5C%2Fwww.technologyreview.com%5C%2F2020%5C%2F11%5C%2F12%5C%2F1011944%5C%2Fartificial-intelligence-replication-crisis-science-big-tech-google-deepmind-facebook-openai%5C%2F%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D4IVIKSUK%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22AI%20is%20wrestling%20with%20a%20replication%20crisis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Will%20Douglass%22%2C%22lastName%22%3A%22Heaven%22%7D%5D%2C%22abstractNote%22%3A%22%5BBeginning%20of%20article%3A%5D%20Last%20month%20Nature%20published%20a%20damning%20response%20written%20by%2031%20scientists%20to%20a%20study%20from%20Google%20Health%20that%20had%20appeared%20in%20the%20journal%20earlier%20this%20year.%20Google%20was%20describing%20successful%20trials%20of%20an%20AI%20that%20looked%20for%20signs%20of%20breast%20cancer%20in%20medical%20images.%20But%20according%20to%20its%20critics%2C%20the%20Google%20team%20provided%20so%20little%20information%20about%20its%20code%20and%20how%20it%20was%20tested%20that%20the%20study%20amounted%20to%20nothing%20more%20than%20a%20promotion%20of%20proprietary%20tech.%5Cn%5Cn%5Cu201cWe%20couldn%5Cu2019t%20take%20it%20anymore%2C%5Cu201d%20says%20Benjamin%20Haibe-Kains%2C%20the%20lead%20author%20of%20the%20response%2C%20who%20studies%20computational%20genomics%20at%20the%20University%20of%20Toronto.%20%5Cu201cIt%5Cu2019s%20not%20about%20this%20study%20in%20particular%5Cu2014it%5Cu2019s%20a%20trend%20we%5Cu2019ve%20been%20witnessing%20for%20multiple%20years%20now%20that%20has%20started%20to%20really%20bother%20us.%5Cu201d%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.technologyreview.com%5C%2F2020%5C%2F11%5C%2F12%5C%2F1011944%5C%2Fartificial-intelligence-replication-crisis-science-big-tech-google-deepmind-facebook-openai%5C%2F%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-11-14T06%3A21%3A30Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Reproducibility%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22VCQ8ZIXE%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Kwak%20et%20al.%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EKwak%2C%20Haewoon%2C%20Jisun%20An%2C%20and%20Yong-Yeol%20Ahn.%20%26%23x201C%3BA%20Systematic%20Media%20Frame%20Analysis%20of%201.5%20Million%20New%20York%20Times%20Articles%20from%202000%20to%202017.%26%23x201D%3B%20%3Ci%3EArXiv%3A2005.01803%20%5BCs%5D%3C%5C%2Fi%3E%2C%202020.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2005.01803%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2005.01803%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DVCQ8ZIXE%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20Systematic%20Media%20Frame%20Analysis%20of%201.5%20Million%20New%20York%20Times%20Articles%20from%202000%20to%202017%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Haewoon%22%2C%22lastName%22%3A%22Kwak%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jisun%22%2C%22lastName%22%3A%22An%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yong-Yeol%22%2C%22lastName%22%3A%22Ahn%22%7D%5D%2C%22abstractNote%22%3A%22Framing%20is%20an%20indispensable%20narrative%20device%20for%20news%20media%20because%20even%20the%20same%20facts%20may%20lead%20to%20conflicting%20understandings%20if%20deliberate%20framing%20is%20employed.%20Therefore%2C%20identifying%20media%20framing%20is%20a%20crucial%20step%20to%20understanding%20how%20news%20media%20influence%20the%20public.%20Framing%20is%2C%20however%2C%20difficult%20to%20operationalize%20and%20detect%2C%20and%20thus%20traditional%20media%20framing%20studies%20had%20to%20rely%20on%20manual%20annotation%2C%20which%20is%20challenging%20to%20scale%20up%20to%20massive%20news%20datasets.%20Here%2C%20by%20developing%20a%20media%20frame%20classifier%20that%20achieves%20state-of-the-art%20performance%2C%20we%20systematically%20analyze%20the%20media%20frames%20of%201.5%20million%20New%20York%20Times%20articles%20published%20from%202000%20to%202017.%20By%20examining%20the%20ebb%20and%20flow%20of%20media%20frames%20over%20almost%20two%20decades%2C%20we%20show%20that%20short-term%20frame%20abundance%20fluctuation%20closely%20corresponds%20to%20major%20events%2C%20while%20there%20also%20exist%20several%20long-term%20trends%2C%20such%20as%20the%20gradually%20increasing%20prevalence%20of%20the%20%60%60Cultural%20identity%27%27%20frame.%20By%20examining%20specific%20topics%20and%20sentiments%2C%20we%20identify%20characteristics%20and%20dynamics%20of%20each%20frame.%20Finally%2C%20as%20a%20case%20study%2C%20we%20delve%20into%20the%20framing%20of%20mass%20shootings%2C%20revealing%20three%20major%20framing%20patterns.%20Our%20scalable%2C%20computational%20approach%20to%20massive%20news%20datasets%20opens%20up%20new%20pathways%20for%20systematic%20media%20framing%20studies.%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2005.01803%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-08-15T23%3A13%3A36Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Frame%20analysis%20of%20media%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Text%20classification%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22WHBTVD5K%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A1550555%2C%22username%22%3A%22nazkey%22%2C%22name%22%3A%22Naz%20Keynejad%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fnazkey%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Dickson%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EDickson%2C%20Ben.%20%26%23x201C%3BThe%20Advantages%20of%20Self-Explainable%20AI%20over%20Interpretable%20AI.%26%23x201D%3B%20The%20Next%20Web%2C%202020.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fthenextweb.com%5C%2Fneural%5C%2F2020%5C%2F06%5C%2F19%5C%2Fthe-advantages-of-self-explainable-ai-over-interpretable-ai%5C%2F%27%3Ehttps%3A%5C%2F%5C%2Fthenextweb.com%5C%2Fneural%5C%2F2020%5C%2F06%5C%2F19%5C%2Fthe-advantages-of-self-explainable-ai-over-interpretable-ai%5C%2F%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DWHBTVD5K%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22webpage%22%2C%22title%22%3A%22The%20advantages%20of%20self-explainable%20AI%20over%20interpretable%20AI%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ben%22%2C%22lastName%22%3A%22Dickson%22%7D%5D%2C%22abstractNote%22%3A%22%5BBeginning%20of%20article%3A%5D%20Would%20you%20trust%20an%20artificial%20intelligence%20algorithm%20that%20works%20eerily%20well%2C%20making%20accurate%20decisions%2099.9%25%20of%20the%20time%2C%20but%20is%20a%20mysterious%20black%20box%3F%20Every%20system%20fails%20every%20now%20and%20then%2C%20and%20when%20it%20does%2C%20we%20want%20explanations%2C%20especially%20when%20human%20lives%20are%20at%20stake.%20And%20a%20system%20that%20can%5Cu2019t%20be%20explained%20can%5Cu2019t%20be%20trusted.%20That%20is%20one%20of%20the%20problems%20the%20AI%20community%20faces%20as%20their%20creations%20become%20smarter%20and%20more%20capable%20of%20tackling%20complicated%20and%20critical%20tasks.%22%2C%22date%22%3A%222020%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fthenextweb.com%5C%2Fneural%5C%2F2020%5C%2F06%5C%2F19%5C%2Fthe-advantages-of-self-explainable-ai-over-interpretable-ai%5C%2F%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-07-13T18%3A04%3A24Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22MS4U5EAW%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Rogers%20et%20al.%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ERogers%2C%20Anna%2C%20Olga%20Kovaleva%2C%20and%20Anna%20Rumshisky.%20%26%23x201C%3BA%20Primer%20in%20BERTology%3A%20What%20We%20Know%20about%20How%20BERT%20Works.%26%23x201D%3B%20%3Ci%3EArXiv%3A2002.12327%20%5BCs%5D%3C%5C%2Fi%3E%2C%202020.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DMS4U5EAW%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20Primer%20in%20BERTology%3A%20What%20we%20know%20about%20how%20BERT%20works%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anna%22%2C%22lastName%22%3A%22Rogers%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Olga%22%2C%22lastName%22%3A%22Kovaleva%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anna%22%2C%22lastName%22%3A%22Rumshisky%22%7D%5D%2C%22abstractNote%22%3A%22Transformer-based%20models%20are%20now%20widely%20used%20in%20NLP%2C%20but%20we%20still%20do%20not%20understand%20a%20lot%20about%20their%20inner%20workings.%20This%20paper%20describes%20what%20is%20known%20to%20date%20about%20the%20famous%20BERT%20model%20%28Devlin%20et%20al.%202019%29%2C%20synthesizing%20over%2040%20analysis%20studies.%20We%20also%20provide%20an%20overview%20of%20the%20proposed%20modifications%20to%20the%20model%20and%20its%20training%20regime.%20We%20then%20outline%20the%20directions%20for%20further%20research.%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-29T06%3A39%3A16Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Text%20Analysis%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22HK5Z5SCM%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Munro%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EMunro%2C%20Robert.%20%3Ci%3EHuman-in-the-Loop%20Machine%20Learning%3C%5C%2Fi%3E.%20Shelter%20Island%2C%20New%20York%3A%20Manning%2C%202020.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.manning.com%5C%2Fbooks%5C%2Fhuman-in-the-loop-machine-learning%27%3Ehttps%3A%5C%2F%5C%2Fwww.manning.com%5C%2Fbooks%5C%2Fhuman-in-the-loop-machine-learning%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DHK5Z5SCM%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Human-in-the-Loop%20Machine%20Learning%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Robert%22%2C%22lastName%22%3A%22Munro%22%7D%5D%2C%22abstractNote%22%3A%22Most%20machine%20learning%20systems%20that%20are%20deployed%20in%20the%20world%20today%20learn%20from%20human%20feedback.%20However%2C%20most%20machine%20learning%20courses%20focus%20almost%20exclusively%20on%20the%20algorithms%2C%20not%20the%20human-computer%20interaction%20part%20of%20the%20systems.%20This%20can%20leave%20a%20big%20knowledge%20gap%20for%20data%20scientists%20working%20in%20real-world%20machine%20learning%2C%20where%20data%20scientists%20spend%20more%20time%20on%20data%20management%20than%20on%20building%20algorithms.%20Human-in-the-Loop%20Machine%20Learning%20is%20a%20practical%20guide%20to%20optimizing%20the%20entire%20machine%20learning%20process%2C%20including%20techniques%20for%20annotation%2C%20active%20learning%2C%20transfer%20learning%2C%20and%20using%20machine%20learning%20to%20optimize%20every%20step%20of%20the%20process.%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22978-1-61729-674-1%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.manning.com%5C%2Fbooks%5C%2Fhuman-in-the-loop-machine-learning%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-08T21%3A13%3A04Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22NUB9UU29%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Selbst%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESelbst%2C%20Andrew%20D.%2C%20Danah%20Boyd%2C%20Sorelle%20A.%20Friedler%2C%20Suresh%20Venkatasubramanian%2C%20and%20Janet%20Vertesi.%20%26%23x201C%3BFairness%20and%20Abstraction%20in%20Sociotechnical%20Systems.%26%23x201D%3B%20In%20%3Ci%3EProceedings%20of%20the%20Conference%20on%20Fairness%2C%20Accountability%2C%20and%20Transparency%3C%5C%2Fi%3E%2C%2059%26%23x2013%3B68.%20FAT%2A%20%26%23x2019%3B19.%20Atlanta%2C%20GA%2C%20USA%3A%20Association%20for%20Computing%20Machinery%2C%202019.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3287560.3287598%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3287560.3287598%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DNUB9UU29%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Fairness%20and%20Abstraction%20in%20Sociotechnical%20Systems%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrew%20D.%22%2C%22lastName%22%3A%22Selbst%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Danah%22%2C%22lastName%22%3A%22Boyd%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sorelle%20A.%22%2C%22lastName%22%3A%22Friedler%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Suresh%22%2C%22lastName%22%3A%22Venkatasubramanian%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Janet%22%2C%22lastName%22%3A%22Vertesi%22%7D%5D%2C%22abstractNote%22%3A%22A%20key%20goal%20of%20the%20fair-ML%20community%20is%20to%20develop%20machine-learning%20based%20systems%20that%2C%20once%20introduced%20into%20a%20social%20context%2C%20can%20achieve%20social%20and%20legal%20outcomes%20such%20as%20fairness%2C%20justice%2C%20and%20due%20process.%20Bedrock%20concepts%20in%20computer%20science---such%20as%20abstraction%20and%20modular%20design---are%20used%20to%20define%20notions%20of%20fairness%20and%20discrimination%2C%20to%20produce%20fairness-aware%20learning%20algorithms%2C%20and%20to%20intervene%20at%20different%20stages%20of%20a%20decision-making%20pipeline%20to%20produce%20%5C%22fair%5C%22%20outcomes.%20In%20this%20paper%2C%20however%2C%20we%20contend%20that%20these%20concepts%20render%20technical%20interventions%20ineffective%2C%20inaccurate%2C%20and%20sometimes%20dangerously%20misguided%20when%20they%20enter%20the%20societal%20context%20that%20surrounds%20decision-making%20systems.%20We%20outline%20this%20mismatch%20with%20five%20%5C%22traps%5C%22%20that%20fair-ML%20work%20can%20fall%20into%20even%20as%20it%20attempts%20to%20be%20more%20context-aware%20in%20comparison%20to%20traditional%20data%20science.%20We%20draw%20on%20studies%20of%20sociotechnical%20systems%20in%20Science%20and%20Technology%20Studies%20to%20explain%20why%20such%20traps%20occur%20and%20how%20to%20avoid%20them.%20Finally%2C%20we%20suggest%20ways%20in%20which%20technical%20designers%20can%20mitigate%20the%20traps%20through%20a%20refocusing%20of%20design%20in%20terms%20of%20process%20rather%20than%20solutions%2C%20and%20by%20drawing%20abstraction%20boundaries%20to%20include%20social%20actors%20rather%20than%20purely%20technical%20ones.%22%2C%22date%22%3A%222019%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%20Conference%20on%20Fairness%2C%20Accountability%2C%20and%20Transparency%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%2210.1145%5C%2F3287560.3287598%22%2C%22ISBN%22%3A%22978-1-4503-6125-5%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3287560.3287598%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-09-14T23%3A37%3A51Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22DSXGKU6A%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Rudin%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ERudin%2C%20Cynthia.%20%26%23x201C%3BStop%20Explaining%20Black%20Box%20Machine%20Learning%20Models%20for%20High%20Stakes%20Decisions%20and%20Use%20Interpretable%20Models%20Instead.%26%23x201D%3B%20%3Ci%3ENature%20Machine%20Intelligence%3C%5C%2Fi%3E%201%2C%20no.%205%20%282019%29%3A%20206%26%23x2013%3B15.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1038%5C%2Fs42256-019-0048-x%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1038%5C%2Fs42256-019-0048-x%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DDSXGKU6A%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Stop%20explaining%20black%20box%20machine%20learning%20models%20for%20high%20stakes%20decisions%20and%20use%20interpretable%20models%20instead%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cynthia%22%2C%22lastName%22%3A%22Rudin%22%7D%5D%2C%22abstractNote%22%3A%22Black%20box%20machine%20learning%20models%20are%20currently%20being%20used%20for%20high-stakes%20decision%20making%20throughout%20society%2C%20causing%20problems%20in%20healthcare%2C%20criminal%20justice%20and%20other%20domains.%20Some%20people%20hope%20that%20creating%20methods%20for%20explaining%20these%20black%20box%20models%20will%20alleviate%20some%20of%20the%20problems%2C%20but%20trying%20to%20explain%20black%20box%20models%2C%20rather%20than%20creating%20models%20that%20are%20interpretable%20in%20the%20first%20place%2C%20is%20likely%20to%20perpetuate%20bad%20practice%20and%20can%20potentially%20cause%20great%20harm%20to%20society.%20The%20way%20forward%20is%20to%20design%20models%20that%20are%20inherently%20interpretable.%20This%20Perspective%20clarifies%20the%20chasm%20between%20explaining%20black%20boxes%20and%20using%20inherently%20interpretable%20models%2C%20outlines%20several%20key%20reasons%20why%20explainable%20black%20boxes%20should%20be%20avoided%20in%20high-stakes%20decisions%2C%20identifies%20challenges%20to%20interpretable%20machine%20learning%2C%20and%20provides%20several%20example%20applications%20where%20interpretable%20models%20could%20potentially%20replace%20black%20box%20models%20in%20criminal%20justice%2C%20healthcare%20and%20computer%20vision.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1038%5C%2Fs42256-019-0048-x%22%2C%22ISSN%22%3A%222522-5839%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.nature.com%5C%2Farticles%5C%2Fs42256-019-0048-x%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-08-12T19%3A06%3A26Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22EU7LY55P%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Molnar%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EMolnar%2C%20Christoph.%20%3Ci%3EInterpretable%20Machine%20Learning%3C%5C%2Fi%3E.%20Christoph%20Molnar%2C%202019.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fchristophm.github.io%5C%2Finterpretable-ml-book%5C%2F%27%3Ehttps%3A%5C%2F%5C%2Fchristophm.github.io%5C%2Finterpretable-ml-book%5C%2F%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DEU7LY55P%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Interpretable%20Machine%20Learning%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Christoph%22%2C%22lastName%22%3A%22Molnar%22%7D%5D%2C%22abstractNote%22%3A%22Molnar%5Cu2019s%20book%20is%20about%20making%20machine%20learning%20models%20and%20their%20decisions%20interpretable.%20After%20exploring%20the%20concepts%20of%20interpretability%2C%20he%20informs%20the%20reader%20about%20simple%2C%20interpretable%20models%20such%20as%20decision%20trees%2C%20decision%20rules%20and%20linear%20regression.%20Later%20chapters%20focus%20on%20general%20model-agnostic%20methods%20for%20interpreting%20black%20box%20models%20like%20feature%20importance%20and%20accumulated%20local%20effects%20and%20explaining%20individual%20predictions%20with%20Shapley%20values%20and%20LIME.%20All%20interpretation%20methods%20are%20explained%20in%20depth%20and%20discussed%20critically.%20Molnar%5Cu2019s%20book%20helps%20the%20reader%20to%20select%20and%20correctly%20apply%20the%20interpretation%20method%20that%20is%20most%20suitable%20for%20your%20machine%20learning%20project.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fchristophm.github.io%5C%2Finterpretable-ml-book%5C%2F%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-07-01T20%3A54%3A25Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22RIYZUJJ9%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Murdoch%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EMurdoch%2C%20W.%20James%2C%20Chandan%20Singh%2C%20Karl%20Kumbier%2C%20Reza%20Abbasi-Asl%2C%20and%20Bin%20Yu.%20%26%23x201C%3BInterpretable%20Machine%20Learning%3A%20Definitions%2C%20Methods%2C%20and%20Applications.%26%23x201D%3B%20%3Ci%3EArXiv%3A1901.04592%20%5BCs%2C%20Stat%5D%3C%5C%2Fi%3E%2C%202019.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1901.04592%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1901.04592%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DRIYZUJJ9%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Interpretable%20machine%20learning%3A%20definitions%2C%20methods%2C%20and%20applications%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22W.%20James%22%2C%22lastName%22%3A%22Murdoch%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Chandan%22%2C%22lastName%22%3A%22Singh%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Karl%22%2C%22lastName%22%3A%22Kumbier%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Reza%22%2C%22lastName%22%3A%22Abbasi-Asl%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Bin%22%2C%22lastName%22%3A%22Yu%22%7D%5D%2C%22abstractNote%22%3A%22The%20authors%20aim%20to%20address%20concerns%20surrounding%20machine-learning%20models%20by%20defining%20interpretability%20in%20the%20context%20of%20machine%20learning%20and%20introducing%20the%20Predictive%2C%20Descriptive%2C%20Relevant%20%28PDR%29%20framework%20for%20discussing%20interpretations.%20The%20PDR%20framework%20provides%20three%20overarching%20desiderata%20for%20evaluation%3A%20predictive%20accuracy%2C%20descriptive%20accuracy%20and%20relevancy%2C%20with%20relevancy%20judged%20relative%20to%20a%20human%20audience.%20Moreover%2C%20to%20help%20manage%20the%20deluge%20of%20interpretation%20methods%2C%20they%20introduce%20a%20categorization%20of%20existing%20techniques%20into%20model-based%20and%20post-hoc%20categories%2C%20with%20sub-groups%20including%20sparsity%2C%20modularity%20and%20simulatability.%20To%20demonstrate%20how%20practitioners%20can%20use%20the%20PDR%20framework%20to%20evaluate%20and%20understand%20interpretations%2C%20the%20authors%20provide%20numerous%20real-world%20examples%20that%20highlight%20the%20often%20under-appreciated%20role%20played%20by%20human%20audiences%20in%20discussions%20of%20interpretability.%20Finally%2C%20based%20on%20their%20framework%2C%20the%20authors%20discuss%20limitations%20of%20existing%20methods%20and%20directions%20for%20future%20work.%22%2C%22date%22%3A%222019%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1901.04592%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A42%3A37Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22NEBXYM6C%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Shu%20et%20al.%22%2C%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EShu%2C%20Kai%2C%20Suhang%20Wang%2C%20and%20Huan%20Liu.%20%26%23x201C%3BBeyond%20News%20Contents%3A%20The%20Role%20of%20Social%20Context%20for%20Fake%20News%20Detection.%26%23x201D%3B%20In%20%3Ci%3EProceedings%20of%20the%20Twelfth%20ACM%20International%20Conference%20on%20Web%20Search%20and%20Data%20Mining%3C%5C%2Fi%3E%2C%20312%26%23x2013%3B20.%20WSDM%20%26%23x2019%3B19.%20Melbourne%20VIC%2C%20Australia%3A%20Association%20for%20Computing%20Machinery%2C%202019.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3289600.3290994%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3289600.3290994%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DNEBXYM6C%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Beyond%20News%20Contents%3A%20The%20Role%20of%20Social%20Context%20for%20Fake%20News%20Detection%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kai%22%2C%22lastName%22%3A%22Shu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Suhang%22%2C%22lastName%22%3A%22Wang%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huan%22%2C%22lastName%22%3A%22Liu%22%7D%5D%2C%22abstractNote%22%3A%22Social%20media%20is%20becoming%20popular%20for%20news%20consumption%20due%20to%20its%20fast%20dissemination%2C%20easy%20access%2C%20and%20low%20cost.%20However%2C%20it%20also%20enables%20the%20wide%20propagation%20of%20fake%20news%2C%20i.e.%2C%20news%20with%20intentionally%20false%20information.%20Detecting%20fake%20news%20is%20an%20important%20task%2C%20which%20not%20only%20ensures%20users%20receive%20authentic%20information%20but%20also%20helps%20maintain%20a%20trustworthy%20news%20ecosystem.%20The%20majority%20of%20existing%20detection%20algorithms%20focus%20on%20finding%20clues%20from%20news%20contents%2C%20which%20are%20generally%20not%20effective%20because%20fake%20news%20is%20often%20intentionally%20written%20to%20mislead%20users%20by%20mimicking%20true%20news.%20Therefore%2C%20we%20need%20to%20explore%20auxiliary%20information%20to%20improve%20detection.%20The%20social%20context%20during%20news%20dissemination%20process%20on%20social%20media%20forms%20the%20inherent%20tri-relationship%2C%20the%20relationship%20among%20publishers%2C%20news%20pieces%2C%20and%20users%2C%20which%20has%20the%20potential%20to%20improve%20fake%20news%20detection.%20For%20example%2C%20partisan-biased%20publishers%20are%20more%20likely%20to%20publish%20fake%20news%2C%20and%20low-credible%20users%20are%20more%20likely%20to%20share%20fake%20news.%20In%20this%20paper%2C%20we%20study%20the%20novel%20problem%20of%20exploiting%20social%20context%20for%20fake%20news%20detection.%20We%20propose%20a%20tri-relationship%20embedding%20framework%20TriFN%2C%20which%20models%20publisher-news%20relations%20and%20user-news%20interactions%20simultaneously%20for%20fake%20news%20classification.%20We%20conduct%20experiments%20on%20two%20real-world%20datasets%2C%20which%20demonstrate%20that%20the%20proposed%20approach%20significantly%20outperforms%20other%20baseline%20methods%20for%20fake%20news%20detection.%22%2C%22date%22%3A%222019%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%20Twelfth%20ACM%20International%20Conference%20on%20Web%20Search%20and%20Data%20Mining%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1145%5C%2F3289600.3290994%22%2C%22ISBN%22%3A%22978-1-4503-5940-5%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3289600.3290994%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T08%3A13%3A36Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Network%20analysis%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22MNPZPLYJ%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22parsedDate%22%3A%222019%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3E%26%23x201C%3BMilestones%3ADIALOG%20Online%20Search%20System%2C%201966%20-%20Engineering%20and%20Technology%20History%20Wiki%2C%26%23x201D%3B%202019.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fethw.org%5C%2FMilestones%3ADIALOG_Online_Search_System%2C_1966%27%3Ehttps%3A%5C%2F%5C%2Fethw.org%5C%2FMilestones%3ADIALOG_Online_Search_System%2C_1966%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DMNPZPLYJ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22webpage%22%2C%22title%22%3A%22Milestones%3ADIALOG%20Online%20Search%20System%2C%201966%20-%20Engineering%20and%20Technology%20History%20Wiki%22%2C%22creators%22%3A%5B%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222019%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fethw.org%5C%2FMilestones%3ADIALOG_Online_Search_System%2C_1966%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-30T20%3A33%3A52Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22Z6VGKZCS%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Narayanan%20et%20al.%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ENarayanan%2C%20Menaka%2C%20Emily%20Chen%2C%20Jeffrey%20He%2C%20Been%20Kim%2C%20Sam%20Gershman%2C%20and%20Finale%20Doshi-Velez.%20%26%23x201C%3BHow%20Do%20Humans%20Understand%20Explanations%20from%20Machine%20Learning%20Systems%3F%20An%20Evaluation%20of%20the%20Human-Interpretability%20of%20Explanation.%26%23x201D%3B%20%3Ci%3EArXiv%3A1802.00682%20%5BCs%5D%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1802.00682%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1802.00682%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DZ6VGKZCS%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22How%20do%20Humans%20Understand%20Explanations%20from%20Machine%20Learning%20Systems%3F%20An%20Evaluation%20of%20the%20Human-Interpretability%20of%20Explanation%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Menaka%22%2C%22lastName%22%3A%22Narayanan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Emily%22%2C%22lastName%22%3A%22Chen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jeffrey%22%2C%22lastName%22%3A%22He%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Been%22%2C%22lastName%22%3A%22Kim%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sam%22%2C%22lastName%22%3A%22Gershman%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Finale%22%2C%22lastName%22%3A%22Doshi-Velez%22%7D%5D%2C%22abstractNote%22%3A%22Recent%20years%20have%20seen%20a%20boom%20in%20interest%20in%20machine%20learning%20systems%20that%20can%20provide%20a%20human-understandable%20rationale%20for%20their%20predictions%20or%20decisions.%20However%2C%20exactly%20what%20kinds%20of%20explanation%20are%20truly%20human-interpretable%20remains%20poorly%20understood.%20This%20work%20advances%20our%20understanding%20of%20what%20makes%20explanations%20interpretable%20in%20the%20specific%20context%20of%20verification.%20Suppose%20we%20have%20a%20machine%20learning%20system%20that%20predicts%20X%2C%20and%20we%20provide%20rationale%20for%20this%20prediction%20X.%20Given%20an%20input%2C%20an%20explanation%2C%20and%20an%20output%2C%20is%20the%20output%20consistent%20with%20the%20input%20and%20the%20supposed%20rationale%3F%20Via%20a%20series%20of%20user-studies%2C%20we%20identify%20what%20kinds%20of%20increases%20in%20complexity%20have%20the%20greatest%20effect%20on%20the%20time%20it%20takes%20for%20humans%20to%20verify%20the%20rationale%2C%20and%20which%20seem%20relatively%20insensitive.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1802.00682%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-27T08%3A57%3A54Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22KAZPSZE3%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Selbst%20and%20Barocas%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESelbst%2C%20Andrew%20D.%2C%20and%20Solon%20Barocas.%20%26%23x201C%3BThe%20Intuitive%20Appeal%20of%20Explainable%20Machines.%26%23x201D%3B%20%3Ci%3ESSRN%20Electronic%20Journal%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.2139%5C%2Fssrn.3126971%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.2139%5C%2Fssrn.3126971%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DKAZPSZE3%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22The%20Intuitive%20Appeal%20of%20Explainable%20Machines%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Andrew%20D.%22%2C%22lastName%22%3A%22Selbst%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Solon%22%2C%22lastName%22%3A%22Barocas%22%7D%5D%2C%22abstractNote%22%3A%22Algorithmic%20decision-making%20has%20become%20synonymous%20with%20inexplicable%20decision-making%2C%20but%20what%20makes%20algorithms%20so%20difficult%20to%20explain%3F%20This%20Article%20examines%20what%20sets%20machine%20learning%20apart%20from%20other%20ways%20of%20developing%20rules%20for%20decision-making%20and%20the%20problem%20these%20properties%20pose%20for%20explanation.%20We%20show%20that%20machine%20learning%20models%20can%20be%20both%20inscrutable%20and%20nonintuitive%20and%20that%20these%20are%20related%2C%20but%20distinct%2C%20properties.%5Cn%5CnCalls%20for%20explanation%20have%20treated%20these%20problems%20as%20one%20and%20the%20same%2C%20but%20disentangling%20the%20two%20reveals%20that%20they%20demand%20very%20different%20responses.%20Dealing%20with%20inscrutability%20requires%20providing%20a%20sensible%20description%20of%20the%20rules%3B%20addressing%20nonintuitiveness%20requires%20providing%20a%20satisfying%20explanation%20for%20why%20the%20rules%20are%20what%20they%20are.%20Existing%20laws%20like%20the%20Fair%20Credit%20Reporting%20Act%20%28FCRA%29%2C%20the%20Equal%20Credit%20Opportunity%20Act%20%28ECOA%29%2C%20and%20the%20General%20Data%20Protection%20Regulation%20%28GDPR%29%2C%20as%20well%20as%20techniques%20within%20machine%20learning%2C%20are%20focused%20almost%20entirely%20on%20the%20problem%20of%20inscrutability.%20While%20such%20techniques%20could%20allow%20a%20machine%20learning%20system%20to%20comply%20with%20existing%20law%2C%20doing%20so%20may%20not%20help%20if%20the%20goal%20is%20to%20assess%20whether%20the%20basis%20for%20decision-making%20is%20normatively%20defensible.%5Cn%5CnIn%20most%20cases%2C%20intuition%20serves%20as%20the%20unacknowledged%20bridge%20between%20a%20descriptive%20account%20and%20a%20normative%20evaluation.%20But%20because%20machine%20learning%20is%20often%20valued%20for%20its%20ability%20to%20uncover%20statistical%20relationships%20that%20defy%20intuition%2C%20relying%20on%20intuition%20is%20not%20a%20satisfying%20approach.%20This%20Article%20thus%20argues%20for%20other%20mechanisms%20for%20normative%20evaluation.%20To%20know%20why%20the%20rules%20are%20what%20they%20are%2C%20one%20must%20seek%20explanations%20of%20the%20process%20behind%20a%20model%5Cu2019s%20development%2C%20not%20just%20explanations%20of%20the%20model%20itself.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.2139%5C%2Fssrn.3126971%22%2C%22ISSN%22%3A%221556-5068%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.ssrn.com%5C%2Fabstract%3D3126971%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-27T08%3A56%3A34Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%228UEU8HL4%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Hind%20et%20al.%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EHind%2C%20Michael%2C%20Dennis%20Wei%2C%20Murray%20Campbell%2C%20Noel%20C.%20F.%20Codella%2C%20Amit%20Dhurandhar%2C%20Aleksandra%20Mojsilovi%26%23x107%3B%2C%20Karthikeyan%20Natesan%20Ramamurthy%2C%20and%20Kush%20R.%20Varshney.%20%26%23x201C%3BTED%3A%20Teaching%20AI%20to%20Explain%20Its%20Decisions.%26%23x201D%3B%20%3Ci%3EArXiv%3A1811.04896%20%5BCs%5D%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1811.04896%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1811.04896%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D8UEU8HL4%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22TED%3A%20Teaching%20AI%20to%20Explain%20its%20Decisions%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Hind%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dennis%22%2C%22lastName%22%3A%22Wei%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Murray%22%2C%22lastName%22%3A%22Campbell%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Noel%20C.%20F.%22%2C%22lastName%22%3A%22Codella%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Amit%22%2C%22lastName%22%3A%22Dhurandhar%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Aleksandra%22%2C%22lastName%22%3A%22Mojsilovi%5Cu0107%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Karthikeyan%20Natesan%22%2C%22lastName%22%3A%22Ramamurthy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kush%20R.%22%2C%22lastName%22%3A%22Varshney%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20systems%20are%20being%20increasingly%20deployed%20due%20to%20their%20potential%20to%20increase%20the%20efficiency%2C%20scale%2C%20consistency%2C%20fairness%2C%20and%20accuracy%20of%20decisions.%20However%2C%20as%20many%20of%20these%20systems%20are%20opaque%20in%20their%20operation%2C%20there%20is%20a%20growing%20demand%20for%20such%20systems%20to%20provide%20explanations%20for%20their%20decisions.%20Conventional%20approaches%20to%20this%20problem%20attempt%20to%20expose%20or%20discover%20the%20inner%20workings%20of%20a%20machine%20learning%20model%20with%20the%20hope%20that%20the%20resulting%20explanations%20will%20be%20meaningful%20to%20the%20consumer.%20In%20contrast%2C%20this%20paper%20suggests%20a%20new%20approach%20to%20this%20problem.%20It%20introduces%20a%20simple%2C%20practical%20framework%2C%20called%20Teaching%20Explanations%20for%20Decisions%20%28TED%29%2C%20that%20provides%20meaningful%20explanations%20that%20match%20the%20mental%20model%20of%20the%20consumer.%20We%20illustrate%20the%20generality%20and%20effectiveness%20of%20this%20approach%20with%20two%20different%20examples%2C%20resulting%20in%20highly%20accurate%20explanations%20with%20no%20loss%20of%20prediction%20accuracy%20for%20these%20two%20examples.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1811.04896%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-08-09T19%3A01%3A21Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22HI7SBGVB%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Alvarez-Melis%20and%20Jaakkola%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EAlvarez-Melis%2C%20David%2C%20and%20Tommi%20Jaakkola.%20%26%23x201C%3BTowards%20Robust%20Interpretability%20with%20Self-Explaining%20Neural%20Networks.%26%23x201D%3B%20In%20%3Ci%3EAdvances%20in%20Neural%20Information%20Processing%20Systems%2031%3C%5C%2Fi%3E%2C%20edited%20by%20S.%20Bengio%2C%20H.%20Wallach%2C%20H.%20Larochelle%2C%20K.%20Grauman%2C%20N.%20Cesa-Bianchi%2C%20and%20R.%20Garnett%2C%207775%26%23x2013%3B84.%20Curran%20Associates%2C%20Inc.%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F8003-towards-robust-interpretability-with-self-explaining-neural-networks.pdf%27%3Ehttp%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F8003-towards-robust-interpretability-with-self-explaining-neural-networks.pdf%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DHI7SBGVB%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Towards%20Robust%20Interpretability%20with%20Self-Explaining%20Neural%20Networks%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%22%2C%22lastName%22%3A%22Alvarez-Melis%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tommi%22%2C%22lastName%22%3A%22Jaakkola%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22S.%22%2C%22lastName%22%3A%22Bengio%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22H.%22%2C%22lastName%22%3A%22Wallach%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22H.%22%2C%22lastName%22%3A%22Larochelle%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22K.%22%2C%22lastName%22%3A%22Grauman%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22N.%22%2C%22lastName%22%3A%22Cesa-Bianchi%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22R.%22%2C%22lastName%22%3A%22Garnett%22%7D%5D%2C%22abstractNote%22%3A%22Most%20recent%20work%20on%20interpretability%20of%20complex%20machine%20learning%20models%20has%20focused%20on%20estimating%20a-posteriori%20explanations%20for%20previously%20trained%20models%20around%20specific%20predictions.%20Self-explaining%20models%20where%20interpretability%20plays%20a%20key%20role%20already%20during%20learning%20have%20received%20much%20less%20attention.%20We%20propose%20three%20desiderata%20for%20explanations%20in%20general%20--%20explicitness%2C%20faithfulness%2C%20and%20stability%20--%20and%20show%20that%20existing%20methods%20do%20not%20satisfy%20them.%20In%20response%2C%20we%20design%20self-explaining%20models%20in%20stages%2C%20progressively%20generalizing%20linear%20classifiers%20to%20complex%20yet%20architecturally%20explicit%20models.%20Faithfulness%20and%20stability%20are%20enforced%20via%20regularization%20specifically%20tailored%20to%20such%20models.%20Experimental%20results%20across%20various%20benchmark%20datasets%20show%20that%20our%20framework%20offers%20a%20promising%20direction%20for%20reconciling%20model%20complexity%20and%20interpretability.%22%2C%22date%22%3A%222018%22%2C%22proceedingsTitle%22%3A%22Advances%20in%20Neural%20Information%20Processing%20Systems%2031%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fpapers.nips.cc%5C%2Fpaper%5C%2F8003-towards-robust-interpretability-with-self-explaining-neural-networks.pdf%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-08-09T19%3A12%3A44Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22PTNAWWJA%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Gall%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGall%2C%20Richard.%20%3Ci%3EMachine%20Learning%20Explainability%20vs%20Interpretability%3A%20Two%20Concepts%20That%20Could%20Help%20Restore%20Trust%20in%20AI%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.kdnuggets.com%5C%2F2018%5C%2F12%5C%2Fmachine-learning-explainability-interpretability-ai.html%27%3Ehttps%3A%5C%2F%5C%2Fwww.kdnuggets.com%5C%2F2018%5C%2F12%5C%2Fmachine-learning-explainability-interpretability-ai.html%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DPTNAWWJA%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Machine%20Learning%20Explainability%20vs%20Interpretability%3A%20Two%20concepts%20that%20could%20help%20restore%20trust%20in%20AI%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Richard%22%2C%22lastName%22%3A%22Gall%22%7D%5D%2C%22abstractNote%22%3A%22This%20blog%20post%20explains%20the%20key%20differences%20between%20explainability%20and%20interpretability%20and%20why%20they%27re%20so%20important%20for%20machine%20learning%20and%20AI%2C%20before%20taking%20a%20look%20at%20several%20techniques%20and%20methods%20for%20improving%20machine%20learning%20interpretability.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.kdnuggets.com%5C%2F2018%5C%2F12%5C%2Fmachine-learning-explainability-interpretability-ai.html%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A40%3A36Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22LMNYM8LH%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Gilpin%20et%20al.%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGilpin%2C%20Leilani%20H.%2C%20David%20Bau%2C%20Ben%20Z.%20Yuan%2C%20Ayesha%20Bajwa%2C%20Michael%20Specter%2C%20and%20Lalana%20Kagal.%20%26%23x201C%3BExplaining%20Explanations%3A%20An%20Overview%20of%20Interpretability%20of%20Machine%20Learning.%26%23x201D%3B%20%3Ci%3EArXiv%3A1806.00069%20%5BCs%2C%20Stat%5D%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1806.00069%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1806.00069%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DLMNYM8LH%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Explaining%20Explanations%3A%20An%20Overview%20of%20Interpretability%20of%20Machine%20Learning%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Leilani%20H.%22%2C%22lastName%22%3A%22Gilpin%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%22%2C%22lastName%22%3A%22Bau%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ben%20Z.%22%2C%22lastName%22%3A%22Yuan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ayesha%22%2C%22lastName%22%3A%22Bajwa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Specter%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lalana%22%2C%22lastName%22%3A%22Kagal%22%7D%5D%2C%22abstractNote%22%3A%22There%20has%20recently%20been%20a%20surge%20of%20work%20in%20explanatory%20artificial%20intelligence%20%28XAI%29.%20This%20research%20area%20tackles%20the%20important%20problem%20that%20complex%20machines%20and%20algorithms%20often%20cannot%20provide%20insights%20into%20their%20behavior%20and%20thought%20processes.%20In%20an%20effort%20to%20create%20best%20practices%20and%20identify%20open%20challenges%2C%20this%20paper%20provides%20a%20definition%20of%20explainability%20and%20shows%20how%20it%20can%20be%20used%20to%20classify%20existing%20literature.%20It%20discuss%20why%20current%20approaches%20to%20explanatory%20methods%20especially%20for%20deep%20neural%20networks%20are%20insufficient%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1806.00069%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A33%3A40Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22TW5J2NDL%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Spencer%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESpencer%2C%20Ann.%20%3Ci%3EMake%20Machine%20Learning%20Interpretability%20More%20Rigorous%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fblog.dominodatalab.com%5C%2Fmake-machine-learning-interpretability-rigorous%5C%2F%27%3Ehttps%3A%5C%2F%5C%2Fblog.dominodatalab.com%5C%2Fmake-machine-learning-interpretability-rigorous%5C%2F%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DTW5J2NDL%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Make%20Machine%20Learning%20Interpretability%20More%20Rigorous%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ann%22%2C%22lastName%22%3A%22Spencer%22%7D%5D%2C%22abstractNote%22%3A%22This%20Domino%20Data%20Science%20Field%20Note%20covers%20a%20proposed%20definition%20of%20machine%20learning%20interpretability%2C%20why%20interpretability%20matters%2C%20and%20the%20arguments%20for%20considering%20a%20rigorous%20evaluation%20of%20interpretability.%20Insights%20are%20drawn%20from%20Finale%20Doshi-Velez%5Cu2019s%20talk%2C%20%5Cu201cA%20Roadmap%20for%20the%20Rigorous%20Science%20of%20Interpretability%5Cu201d%20as%20well%20as%20the%20paper%2C%20%5Cu201cTowards%20a%20Rigorous%20Science%20of%20Interpretable%20Machine%20Learning%5Cu201d.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fblog.dominodatalab.com%5C%2Fmake-machine-learning-interpretability-rigorous%5C%2F%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A40%3A54Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22K39ZRMNQ%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Gill%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGill%2C%20Patrick%20Hall.%20Navdeep.%20%3Ci%3EIntroduction%20to%20Machine%20Learning%20Interpretability%3C%5C%2Fi%3E.%20S.l.%3A%20O%26%23x2019%3BReilly%20Media%2C%20Inc.%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fproquest.safaribooksonline.com%5C%2F9781492033158%27%3Ehttps%3A%5C%2F%5C%2Fproquest.safaribooksonline.com%5C%2F9781492033158%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DK39ZRMNQ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Introduction%20to%20Machine%20Learning%20Interpretability%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Patrick%20Hall.%20Navdeep%22%2C%22lastName%22%3A%22Gill%22%7D%5D%2C%22abstractNote%22%3A%22In%20this%20ebook%2C%20Patrick%20Hall%20and%20Navdeep%20Gill%20from%20H2O.ai%20thoroughly%20introduce%20the%20idea%20of%20machine%20learning%20interpretability%20and%20examine%20a%20set%20of%20machine%20learning%20techniques%2C%20algorithms%2C%20and%20models%20to%20help%20data%20scientists%20improve%20the%20accuracy%20of%20their%20predictive%20models%20while%20maintaining%20interpretability.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22978-1-4920-3315-8%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fproquest.safaribooksonline.com%5C%2F9781492033158%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A42%3A51Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22NMJVETXT%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Parikh%20and%20Atrey%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EParikh%2C%20Shivam%20B.%2C%20and%20Pradeep%20K.%20Atrey.%20%26%23x201C%3BMedia-Rich%20Fake%20News%20Detection%3A%20A%20Survey.%26%23x201D%3B%20In%20%3Ci%3E2018%20IEEE%20Conference%20on%20Multimedia%20Information%20Processing%20and%20Retrieval%20%28MIPR%29%3C%5C%2Fi%3E%2C%20436%26%23x2013%3B41%2C%202018.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FMIPR.2018.00093%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FMIPR.2018.00093%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DNMJVETXT%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Media-Rich%20Fake%20News%20Detection%3A%20A%20Survey%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Shivam%20B.%22%2C%22lastName%22%3A%22Parikh%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Pradeep%20K.%22%2C%22lastName%22%3A%22Atrey%22%7D%5D%2C%22abstractNote%22%3A%22Fake%20News%20has%20been%20around%20for%20decades%20and%20with%20the%20advent%20of%20social%20media%20and%20modern%20day%20journalism%20at%20its%20peak%2C%20detection%20of%20media-rich%20fake%20news%20has%20been%20a%20popular%20topic%20in%20the%20research%20community.%20Given%20the%20challenges%20associated%20with%20detecting%20fake%20news%20research%20problem%2C%20researchers%20around%20the%20globe%20are%20trying%20to%20understand%20the%20basic%20characteristics%20of%20the%20problem%20statement.%20This%20paper%20aims%20to%20present%20an%20insight%20on%20characterization%20of%20news%20story%20in%20the%20modern%20diaspora%20combined%20with%20the%20differential%20content%20types%20of%20news%20story%20and%20its%20impact%20on%20readers.%20Subsequently%2C%20we%20dive%20into%20existing%20fake%20news%20detection%20approaches%20that%20are%20heavily%20based%20on%20text-based%20analysis%2C%20and%20also%20describe%20popular%20fake%20news%20data-sets.%20We%20conclude%20the%20paper%20by%20identifying%204%20key%20open%20research%20challenges%20that%20can%20guide%20future%20research.%22%2C%22date%22%3A%222018%22%2C%22proceedingsTitle%22%3A%222018%20IEEE%20Conference%20on%20Multimedia%20Information%20Processing%20and%20Retrieval%20%28MIPR%29%22%2C%22conferenceName%22%3A%222018%20IEEE%20Conference%20on%20Multimedia%20Information%20Processing%20and%20Retrieval%20%28MIPR%29%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1109%5C%2FMIPR.2018.00093%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T08%3A31%3A42Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22EQGHUWAU%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wu%20and%20Liu%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EWu%2C%20Liang%2C%20and%20Huan%20Liu.%20%26%23x201C%3BTracing%20Fake-News%20Footprints%3A%20Characterizing%20Social%20Media%20Messages%20by%20How%20They%20Propagate.%26%23x201D%3B%20In%20%3Ci%3EProceedings%20of%20the%20Eleventh%20ACM%20International%20Conference%20on%20Web%20Search%20and%20Data%20Mining%3C%5C%2Fi%3E%2C%20637%26%23x2013%3B45.%20WSDM%20%26%23x2019%3B18.%20Marina%20Del%20Rey%2C%20CA%2C%20USA%3A%20Association%20for%20Computing%20Machinery%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3159652.3159677%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3159652.3159677%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DEQGHUWAU%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Tracing%20Fake-News%20Footprints%3A%20Characterizing%20Social%20Media%20Messages%20by%20How%20They%20Propagate%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Liang%22%2C%22lastName%22%3A%22Wu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huan%22%2C%22lastName%22%3A%22Liu%22%7D%5D%2C%22abstractNote%22%3A%22When%20a%20message%2C%20such%20as%20a%20piece%20of%20news%2C%20spreads%20in%20social%20networks%2C%20how%20can%20we%20classify%20it%20into%20categories%20of%20interests%2C%20such%20as%20genuine%20or%20fake%20news%3F%20Classification%20of%20social%20media%20content%20is%20a%20fundamental%20task%20for%20social%20media%20mining%2C%20and%20most%20existing%20methods%20regard%20it%20as%20a%20text%20categorization%20problem%20and%20mainly%20focus%20on%20using%20content%20features%2C%20such%20as%20words%20and%20hashtags.%20However%2C%20for%20many%20emerging%20applications%20like%20fake%20news%20and%20rumor%20detection%2C%20it%20is%20very%20challenging%2C%20if%20not%20impossible%2C%20to%20identify%20useful%20features%20from%20content.%20For%20example%2C%20intentional%20spreaders%20of%20fake%20news%20may%20manipulate%20the%20content%20to%20make%20it%20look%20like%20real%20news.%20To%20address%20this%20problem%2C%20this%20paper%20concentrates%20on%20modeling%20the%20propagation%20of%20messages%20in%20a%20social%20network.%20Specifically%2C%20we%20propose%20a%20novel%20approach%2C%20TraceMiner%2C%20to%20%281%29%20infer%20embeddings%20of%20social%20media%20users%20with%20social%20network%20structures%3B%20and%20%282%29%20utilize%20an%20LSTM-RNN%20to%20represent%20and%20classify%20propagation%20pathways%20of%20a%20message.%20Since%20content%20information%20is%20sparse%20and%20noisy%20on%20social%20media%2C%20adopting%20TraceMiner%20allows%20to%20provide%20a%20high%20degree%20of%20classification%20accuracy%20even%20in%20the%20absence%20of%20content%20information.%20Experimental%20results%20on%20real-world%20datasets%20show%20the%20superiority%20over%20state-of-the-art%20approaches%20on%20the%20task%20of%20fake%20news%20detection%20and%20news%20categorization.%22%2C%22date%22%3A%222018%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%20Eleventh%20ACM%20International%20Conference%20on%20Web%20Search%20and%20Data%20Mining%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1145%5C%2F3159652.3159677%22%2C%22ISBN%22%3A%22978-1-4503-5581-0%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3159652.3159677%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A48%3A14Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Network%20analysis%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22SU8HMWNA%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Lipton%20and%20Steinhardt%22%2C%22parsedDate%22%3A%222018%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ELipton%2C%20Zachary%2C%20and%20Jacob%20Steinhardt.%20%3Ci%3ETroubling%20Trends%20in%20Machine%20Learning%20Scholarship.Pdf%3C%5C%2Fi%3E%2C%202018.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.dropbox.com%5C%2Fs%5C%2Fao7c090p8bg1hk3%5C%2FLipton%2520and%2520Steinhardt%2520-%2520Troubling%2520Trends%2520in%2520Machine%2520Learning%2520Scholarship.pdf%3Fdl%3D0%27%3Ehttps%3A%5C%2F%5C%2Fwww.dropbox.com%5C%2Fs%5C%2Fao7c090p8bg1hk3%5C%2FLipton%2520and%2520Steinhardt%2520-%2520Troubling%2520Trends%2520in%2520Machine%2520Learning%2520Scholarship.pdf%3Fdl%3D0%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DSU8HMWNA%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22book%22%2C%22title%22%3A%22Troubling%20Trends%20in%20Machine%20Learning%20Scholarship.pdf%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Zachary%22%2C%22lastName%22%3A%22Lipton%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jacob%22%2C%22lastName%22%3A%22Steinhardt%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20focuses%20on%20the%20following%20four%20patterns%20that%20appear%20to%20be%20trending%20in%20ML%20scholarship%3A%201.Failure%20to%20distinguish%20between%20explanation%20and%20speculation.%202.Failure%20to%20identify%20the%20sources%20of%20empirical%20gains%2C%20e.g.%20emphasizing%20unnecessary%20modifications%20to%20neural%20architectures%20when%20gains%20actually%20stem%20from%20hyper-parameter%20tuning.%203.Mathiness%3A%20the%20use%20of%20mathematics%20that%20obfuscates%20or%20impresses%20rather%20than%20clarifies%2C%20e.g.%20by%20confusing%20technical%20and%20non-technical%20concepts.%204.Misuse%20of%20language%2C%20e.g.%20by%20choosing%20terms%20of%20art%20with%20colloquial%20connotations%20or%20by%20overloading%20established%20technical%20terms.%20This%20paper%20aims%20to%20instigate%20discussion%2C%20answering%20a%20call%20for%20papers%20from%20the%20ICML%20Machine%20Learning%20Debates%20workshop.%20This%20paper%20does%20not%20purport%20to%20offer%20a%20full%20or%20balanced%20viewpoint%20or%20to%20discuss%20the%20overall%20quality%20of%20science%20in%20ML.%22%2C%22date%22%3A%222018%22%2C%22language%22%3A%22en%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.dropbox.com%5C%2Fs%5C%2Fao7c090p8bg1hk3%5C%2FLipton%2520and%2520Steinhardt%2520-%2520Troubling%2520Trends%2520in%2520Machine%2520Learning%2520Scholarship.pdf%3Fdl%3D0%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A40%3A42Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22SQR5EAAL%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Randles%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ERandles%2C%20Bernadette%20M.%2C%20Irene%20V.%20Pasquetto%2C%20Milena%20S.%20Golshan%2C%20and%20Christine%20L.%20Borgman.%20%26%23x201C%3BUsing%20the%20Jupyter%20Notebook%20as%20a%20Tool%20for%20Open%20Science%3A%20An%20Empirical%20Study.%26%23x201D%3B%20In%20%3Ci%3E2017%20ACM%5C%2FIEEE%20Joint%20Conference%20on%20Digital%20Libraries%20%28JCDL%29%3C%5C%2Fi%3E%2C%201%26%23x2013%3B2%2C%202017.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FJCDL.2017.7991618%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FJCDL.2017.7991618%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DSQR5EAAL%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Using%20the%20Jupyter%20Notebook%20as%20a%20Tool%20for%20Open%20Science%3A%20An%20Empirical%20Study%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Bernadette%20M.%22%2C%22lastName%22%3A%22Randles%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Irene%20V.%22%2C%22lastName%22%3A%22Pasquetto%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Milena%20S.%22%2C%22lastName%22%3A%22Golshan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Christine%20L.%22%2C%22lastName%22%3A%22Borgman%22%7D%5D%2C%22abstractNote%22%3A%22As%20scientific%20work%20becomes%20more%20computational%20and%20data-intensive%2C%20research%20processes%20and%20results%20become%20more%20difficult%20to%20interpret%20and%20reproduce.%20In%20this%20poster%2C%20we%20show%20how%20the%20Jupyter%20notebook%2C%20a%20tool%20originally%20designed%20as%20a%20free%20version%20of%20Mathematica%20notebooks%2C%20has%20evolved%20to%20become%20a%20robust%20tool%20for%20scientists%20to%20share%20code%2C%20associated%20computation%2C%20and%20documentation.%22%2C%22date%22%3A%222017%22%2C%22proceedingsTitle%22%3A%222017%20ACM%5C%2FIEEE%20Joint%20Conference%20on%20Digital%20Libraries%20%28JCDL%29%22%2C%22conferenceName%22%3A%222017%20ACM%5C%2FIEEE%20Joint%20Conference%20on%20Digital%20Libraries%20%28JCDL%29%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1109%5C%2FJCDL.2017.7991618%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-09-03T05%3A49%3A56Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20notebooks%22%7D%2C%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Data%20visualization%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Open%20science%22%7D%2C%7B%22tag%22%3A%22Statistics%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22XXRMYCWY%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Lipton%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ELipton%2C%20Zachary%20C.%20%26%23x201C%3BThe%20Mythos%20of%20Model%20Interpretability.%26%23x201D%3B%20%3Ci%3EArXiv%3A1606.03490%20%5BCs%2C%20Stat%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1606.03490%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1606.03490%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DXXRMYCWY%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22The%20Mythos%20of%20Model%20Interpretability%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Zachary%20C.%22%2C%22lastName%22%3A%22Lipton%22%7D%5D%2C%22abstractNote%22%3A%22Supervised%20machine%20learning%20models%20boast%20remarkable%20predictive%20capabilities.%20But%20can%20you%20trust%20your%20model%3F%20Will%20it%20work%20in%20deployment%3F%20What%20else%20can%20it%20tell%20you%20about%20the%20world%3F%20We%20want%20models%20to%20be%20not%20only%20good%2C%20but%20interpretable.%20And%20yet%20the%20task%20of%20interpretation%20appears%20underspecified.%20Papers%20provide%20diverse%20and%20sometimes%20non-overlapping%20motivations%20for%20interpretability%2C%20and%20offer%20myriad%20notions%20of%20what%20attributes%20render%20models%20interpretable.%20Despite%20this%20ambiguity%2C%20many%20papers%20proclaim%20interpretability%20axiomatically%2C%20absent%20further%20explanation.%20In%20this%20paper%2C%20we%20seek%20to%20refine%20the%20discourse%20on%20interpretability.%20First%2C%20we%20examine%20the%20motivations%20underlying%20interest%20in%20interpretability%2C%20finding%20them%20to%20be%20diverse%20and%20occasionally%20discordant.%20Then%2C%20we%20address%20model%20properties%20and%20techniques%20thought%20to%20confer%20interpretability%2C%20identifying%20transparency%20to%20humans%20and%20post-hoc%20explanations%20as%20competing%20notions.%20Throughout%2C%20we%20discuss%20the%20feasibility%20and%20desirability%20of%20different%20notions%2C%20and%20question%20the%20oft-made%20assertions%20that%20linear%20models%20are%20interpretable%20and%20that%20deep%20neural%20networks%20are%20not.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1606.03490%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-27T08%3A57%3A14Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%2268FG7BA8%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Edwards%20and%20Veale%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EEdwards%2C%20Lilian%2C%20and%20Michael%20Veale.%20%26%23x201C%3BSlave%20to%20the%20Algorithm%3F%20Why%20a%20%26%23x2018%3BRight%20to%20an%20Explanation%26%23x2019%3B%20Is%20Probably%20Not%20the%20Remedy%20You%20Are%20Looking%20For.%26%23x201D%3B%20SSRN%20Scholarly%20Paper.%20Rochester%2C%20NY%3A%20Social%20Science%20Research%20Network%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fpapers.ssrn.com%5C%2Fabstract%3D2972855%27%3Ehttps%3A%5C%2F%5C%2Fpapers.ssrn.com%5C%2Fabstract%3D2972855%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D68FG7BA8%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22report%22%2C%22title%22%3A%22Slave%20to%20the%20Algorithm%3F%20Why%20a%20%27Right%20to%20an%20Explanation%27%20Is%20Probably%20Not%20the%20Remedy%20You%20Are%20Looking%20For%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lilian%22%2C%22lastName%22%3A%22Edwards%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Veale%22%7D%5D%2C%22abstractNote%22%3A%22Algorithms%2C%20particularly%20machine%20learning%20%28ML%29%20algorithms%2C%20are%20increasingly%20important%20to%20individuals%5Cu2019%20lives%2C%20but%20have%20caused%20a%20range%20of%20concerns%20revolving%20mainly%20around%20unfairness%2C%20discrimination%20and%20opacity.%20Transparency%20in%20the%20form%20of%20a%20%5Cu201cright%20to%20an%20explanation%5Cu201d%20has%20emerged%20as%20a%20compellingly%20attractive%20remedy%20since%20it%20intuitively%20promises%20to%20open%20the%20algorithmic%20%5Cu201cblack%20box%5Cu201d%20to%20promote%20challenge%2C%20redress%2C%20and%20hopefully%20heightened%20accountability.%20Amidst%20the%20general%20furore%20over%20algorithmic%20bias%20we%20describe%2C%20any%20remedy%20in%20a%20storm%20has%20looked%20attractive.%22%2C%22reportNumber%22%3A%22ID%202972855%22%2C%22reportType%22%3A%22SSRN%20Scholarly%20Paper%22%2C%22institution%22%3A%22Social%20Science%20Research%20Network%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fpapers.ssrn.com%5C%2Fabstract%3D2972855%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-08T01%3A54%3A57Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22MDHKGZNJ%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Samek%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESamek%2C%20Wojciech%2C%20Thomas%20Wiegand%2C%20and%20Klaus-Robert%20M%26%23xFC%3Bller.%20%26%23x201C%3BExplainable%20Artificial%20Intelligence.%26%23x201D%3B%20%3Ci%3EInternational%20Telecommunication%20Union%20Journal%3C%5C%2Fi%3E%2C%20no.%201%20%282017%29%3A%201%26%23x2013%3B10.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.itu.int%5C%2Fen%5C%2Fjournal%5C%2F001%5C%2FPages%5C%2F05.aspx%27%3Ehttps%3A%5C%2F%5C%2Fwww.itu.int%5C%2Fen%5C%2Fjournal%5C%2F001%5C%2FPages%5C%2F05.aspx%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DMDHKGZNJ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Explainable%20Artificial%20Intelligence%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Wojciech%22%2C%22lastName%22%3A%22Samek%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Thomas%22%2C%22lastName%22%3A%22Wiegand%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Klaus-Robert%22%2C%22lastName%22%3A%22M%5Cu00fcller%22%7D%5D%2C%22abstractNote%22%3A%22With%20the%20availability%20of%20large%20databases%20and%20recent%20improvements%20in%20deep%20learning%20methodology%2C%20the%20performance%20of%20AI%20systems%20is%20reaching%2C%20or%20even%20exceeding%2C%20the%20human%20level%20on%20an%20increasing%20number%20of%20complex%20tasks.%20Impressive%20examples%20of%20this%20development%20can%20be%20found%20in%20domains%20such%20as%20image%20classification%2C%20sentiment%20analysis%2C%20speech%20understanding%20or%20strategic%20game%20playing.%20However%2C%20because%20of%20their%20nested%20non-linear%20structure%2C%20these%20highly%20successful%20machine%20learning%20and%20artificial%20intelligence%20models%20are%20usually%20applied%20in%20a%20black-box%20manner%2C%20i.e.%20no%20information%20is%20provided%20about%20what%20exactly%20makes%20them%20arrive%20at%20their%20predictions.%20Since%20this%20lack%20of%20transparency%20can%20be%20a%20major%20drawback%2C%20e.g.%20in%20medical%20applications%2C%20the%20development%20of%20methods%20for%20visualizing%2C%20explaining%20and%20interpreting%20deep%20learning%20models%20has%20recently%20attracted%20increasing%20attention.%20This%20paper%20summarizes%20recent%20developments%20in%20this%20field%20and%20makes%20a%20plea%20for%20more%20interpretability%20in%20artificial%20intelligence.%20Furthermore%2C%20it%20presents%20two%20approaches%20to%20explaining%20predictions%20of%20deep%20learning%20models%2C%20one%20method%20which%20computes%20the%20sensitivity%20of%20the%20prediction%20with%20respect%20to%20changes%20in%20the%20input%20and%20one%20approach%20which%20meaningfully%20decomposes%20the%20decision%20in%20terms%20of%20the%20input%20variables.%20These%20methods%20are%20evaluated%20on%20three%20classification%20tasks.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.itu.int%5C%2Fen%5C%2Fjournal%5C%2F001%5C%2FPages%5C%2F05.aspx%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A33%3A48Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22YI2CGWHW%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Doshi-Velez%20and%20Kim%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EDoshi-Velez%2C%20Finale%2C%20and%20Been%20Kim.%20%26%23x201C%3BTowards%20A%20Rigorous%20Science%20of%20Interpretable%20Machine%20Learning.%26%23x201D%3B%20%3Ci%3EArXiv%3A1702.08608%20%5BCs%2C%20Stat%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.08608%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.08608%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DYI2CGWHW%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Towards%20A%20Rigorous%20Science%20of%20Interpretable%20Machine%20Learning%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Finale%22%2C%22lastName%22%3A%22Doshi-Velez%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Been%22%2C%22lastName%22%3A%22Kim%22%7D%5D%2C%22abstractNote%22%3A%22As%20machine%20learning%20systems%20become%20ubiquitous%2C%20there%20has%20been%20a%20surge%20of%20interest%20in%20interpretable%20machine%20learning%3A%20systems%20that%20provide%20explanation%20for%20their%20outputs.%20However%2C%20despite%20the%20interest%20in%20interpretability%2C%20there%20is%20very%20little%20consensus%20on%20what%20interpretable%20machine%20learning%20is%20and%20how%20it%20should%20be%20measured.%20This%20paper%20defines%20interpretability%20and%20describes%20when%20interpretability%20is%20needed.%20It%20suggests%20a%20taxonomy%20for%20rigorous%20evaluation%20and%20exposes%20open%20questions%20towards%20a%20more%20rigorous%20science%20of%20interpretable%20machine%20learning%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.08608%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A47%3A32Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22QGZX8UXX%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Shu%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EShu%2C%20Kai%2C%20Suhang%20Wang%2C%20and%20Huan%20Liu.%20%26%23x201C%3BExploiting%20Tri-Relationship%20for%20Fake%20News%20Detection.%26%23x201D%3B%20undefined%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fwww.semanticscholar.org%5C%2Fpaper%5C%2FExploiting-Tri-Relationship-for-Fake-News-Detection-Shu-Wang%5C%2F8fd1d13e18c5ef8b57296adab6543cb810c36d81%27%3Ehttps%3A%5C%2F%5C%2Fwww.semanticscholar.org%5C%2Fpaper%5C%2FExploiting-Tri-Relationship-for-Fake-News-Detection-Shu-Wang%5C%2F8fd1d13e18c5ef8b57296adab6543cb810c36d81%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DQGZX8UXX%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22webpage%22%2C%22title%22%3A%22Exploiting%20Tri-Relationship%20for%20Fake%20News%20Detection%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kai%22%2C%22lastName%22%3A%22Shu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Suhang%22%2C%22lastName%22%3A%22Wang%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huan%22%2C%22lastName%22%3A%22Liu%22%7D%5D%2C%22abstractNote%22%3A%22Social%20media%20for%20news%20consumption%20is%20becoming%20popular%20nowadays.%20The%20low%20cost%2C%20easy%20access%20and%20rapid%20information%20dissemination%20of%20social%20media%20bring%20benefits%20for%20people%20to%20seek%20out%20news%20timely.%20However%2C%20it%20also%20causes%20the%20widespread%20of%20fake%20news%2C%20i.e.%2C%20low-quality%20news%20pieces%20that%20are%20intentionally%20fabricated.%20The%20fake%20news%20brings%20about%20several%20negative%20effects%20on%20individual%20consumers%2C%20news%20ecosystem%2C%20and%20even%20society%20trust.%20Previous%20fake%20news%20detection%20methods%20mainly%20focus%20on%20news%20contents%20for%20deception%20classification%20or%20claim%20fact-checking.%20Recent%20Social%20and%20Psychology%20studies%20show%20potential%20importance%20to%20utilize%20social%20media%20data%3A%201%29%20Confirmation%20bias%20effect%20reveals%20that%20consumers%20prefer%20to%20believe%20information%20that%20confirms%20their%20existing%20stances%3B%202%29%20Echo%20chamber%20effect%20suggests%20that%20people%20tend%20to%20follow%20likeminded%20users%20and%20form%20segregated%20communities%20on%20social%20media.%20Even%20though%20users%27%20social%20engagements%20towards%20news%20on%20social%20media%20provide%20abundant%20auxiliary%20information%20for%20better%20detecting%20fake%20news%2C%20but%20existing%20work%20exploiting%20social%20engagements%20is%20rather%20limited.%20In%20this%20paper%2C%20we%20explore%20the%20correlations%20of%20publisher%20bias%2C%20news%20stance%2C%20and%20relevant%20user%20engagements%20simultaneously%2C%20and%20propose%20a%20Tri-Relationship%20Fake%20News%20detection%20framework%20%28TriFN%29.%20We%20also%20provide%20two%20comprehensive%20real-world%20fake%20news%20datasets%20to%20facilitate%20fake%20news%20research.%20Experiments%20on%20these%20datasets%20demonstrate%20the%20effectiveness%20of%20the%20proposed%20approach.%22%2C%22date%22%3A%222017%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.semanticscholar.org%5C%2Fpaper%5C%2FExploiting-Tri-Relationship-for-Fake-News-Detection-Shu-Wang%5C%2F8fd1d13e18c5ef8b57296adab6543cb810c36d81%22%2C%22language%22%3A%22en%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A54%3A01Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22GPG4L6Z2%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Granik%20and%20Mesyura%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGranik%2C%20Mykhailo%2C%20and%20Volodymyr%20Mesyura.%20%26%23x201C%3BFake%20News%20Detection%20Using%20Naive%20Bayes%20Classifier.%26%23x201D%3B%20In%20%3Ci%3E2017%20IEEE%20First%20Ukraine%20Conference%20on%20Electrical%20and%20Computer%20Engineering%20%28UKRCON%29%3C%5C%2Fi%3E%2C%20900%26%23x2013%3B903%2C%202017.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FUKRCON.2017.8100379%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1109%5C%2FUKRCON.2017.8100379%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DGPG4L6Z2%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Fake%20news%20detection%20using%20naive%20Bayes%20classifier%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mykhailo%22%2C%22lastName%22%3A%22Granik%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Volodymyr%22%2C%22lastName%22%3A%22Mesyura%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20shows%20a%20simple%20approach%20for%20fake%20news%20detection%20using%20naive%20Bayes%20classifier.%20This%20approach%20was%20implemented%20as%20a%20software%20system%20and%20tested%20against%20a%20data%20set%20of%20Facebook%20news%20posts.%20We%20achieved%20classification%20accuracy%20of%20approximately%2074%25%20on%20the%20test%20set%20which%20is%20a%20decent%20result%20considering%20the%20relative%20simplicity%20of%20the%20model.%20This%20results%20may%20be%20improved%20in%20several%20ways%2C%20that%20are%20described%20in%20the%20article%20as%20well.%20Received%20results%20suggest%2C%20that%20fake%20news%20detection%20problem%20can%20be%20addressed%20with%20artificial%20intelligence%20methods.%22%2C%22date%22%3A%222017%22%2C%22proceedingsTitle%22%3A%222017%20IEEE%20First%20Ukraine%20Conference%20on%20Electrical%20and%20Computer%20Engineering%20%28UKRCON%29%22%2C%22conferenceName%22%3A%222017%20IEEE%20First%20Ukraine%20Conference%20on%20Electrical%20and%20Computer%20Engineering%20%28UKRCON%29%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1109%5C%2FUKRCON.2017.8100379%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A46%3A07Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22GBZEZKIK%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Tacchini%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ETacchini%2C%20Eugenio%2C%20Gabriele%20Ballarin%2C%20Marco%20L.%20Della%20Vedova%2C%20Stefano%20Moret%2C%20and%20Luca%20de%20Alfaro.%20%26%23x201C%3BSome%20Like%20It%20Hoax%3A%20Automated%20Fake%20News%20Detection%20in%20Social%20Networks.%26%23x201D%3B%20%3Ci%3EArXiv%3A1704.07506%20%5BCs%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1704.07506%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1704.07506%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DGBZEZKIK%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Some%20Like%20it%20Hoax%3A%20Automated%20Fake%20News%20Detection%20in%20Social%20Networks%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eugenio%22%2C%22lastName%22%3A%22Tacchini%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gabriele%22%2C%22lastName%22%3A%22Ballarin%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Marco%20L.%22%2C%22lastName%22%3A%22Della%20Vedova%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Stefano%22%2C%22lastName%22%3A%22Moret%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Luca%22%2C%22lastName%22%3A%22de%20Alfaro%22%7D%5D%2C%22abstractNote%22%3A%22In%20recent%20years%2C%20the%20reliability%20of%20information%20on%20the%20Internet%20has%20emerged%20as%20a%20crucial%20issue%20of%20modern%20society.%20Social%20network%20sites%20%28SNSs%29%20have%20revolutionized%20the%20way%20in%20which%20information%20is%20spread%20by%20allowing%20users%20to%20freely%20share%20content.%20As%20a%20consequence%2C%20SNSs%20are%20also%20increasingly%20used%20as%20vectors%20for%20the%20diffusion%20of%20misinformation%20and%20hoaxes.%20The%20amount%20of%20disseminated%20information%20and%20the%20rapidity%20of%20its%20diffusion%20make%20it%20practically%20impossible%20to%20assess%20reliability%20in%20a%20timely%20manner%2C%20highlighting%20the%20need%20for%20automatic%20hoax%20detection%20systems.%20As%20a%20contribution%20towards%20this%20objective%2C%20we%20show%20that%20Facebook%20posts%20can%20be%20classified%20with%20high%20accuracy%20as%20hoaxes%20or%20non-hoaxes%20on%20the%20basis%20of%20the%20users%20who%20%5C%22liked%5C%22%20them.%20We%20present%20two%20classification%20techniques%2C%20one%20based%20on%20logistic%20regression%2C%20the%20other%20on%20a%20novel%20adaptation%20of%20boolean%20crowdsourcing%20algorithms.%20On%20a%20dataset%20consisting%20of%2015%2C500%20Facebook%20posts%20and%20909%2C236%20users%2C%20we%20obtain%20classification%20accuracies%20exceeding%2099%25%20even%20when%20the%20training%20set%20contains%20less%20than%201%25%20of%20the%20posts.%20We%20further%20show%20that%20our%20techniques%20are%20robust%3A%20they%20work%20even%20when%20we%20restrict%20our%20attention%20to%20the%20users%20who%20like%20both%20hoax%20and%20non-hoax%20posts.%20These%20results%20suggest%20that%20mapping%20the%20diffusion%20pattern%20of%20information%20can%20be%20a%20useful%20component%20of%20automatic%20hoax%20detection%20systems.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1704.07506%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A44%3A26Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22News%20and%20social%20media%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22RKCERR67%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ahmed%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EAhmed%2C%20Hadeer%2C%20Issa%20Traore%2C%20and%20Sherif%20Saad.%20%26%23x201C%3BDetection%20of%20Online%20Fake%20News%20Using%20N-Gram%20Analysis%20and%20Machine%20Learning%20Techniques.%26%23x201D%3B%20In%20%3Ci%3EIntelligent%2C%20Secure%2C%20and%20Dependable%20Systems%20in%20Distributed%20and%20Cloud%20Environments%3C%5C%2Fi%3E%2C%20edited%20by%20Issa%20Traore%2C%20Isaac%20Woungang%2C%20and%20Ahmed%20Awad%2C%20127%26%23x2013%3B38.%20Lecture%20Notes%20in%20Computer%20Science.%20Cham%3A%20Springer%20International%20Publishing%2C%202017.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1007%5C%2F978-3-319-69155-8_9%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1007%5C%2F978-3-319-69155-8_9%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DRKCERR67%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Detection%20of%20Online%20Fake%20News%20Using%20N-Gram%20Analysis%20and%20Machine%20Learning%20Techniques%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Hadeer%22%2C%22lastName%22%3A%22Ahmed%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Issa%22%2C%22lastName%22%3A%22Traore%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sherif%22%2C%22lastName%22%3A%22Saad%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Issa%22%2C%22lastName%22%3A%22Traore%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Isaac%22%2C%22lastName%22%3A%22Woungang%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Ahmed%22%2C%22lastName%22%3A%22Awad%22%7D%5D%2C%22abstractNote%22%3A%22Fake%20news%20is%20a%20phenomenon%20which%20is%20having%20a%20significant%20impact%20on%20our%20social%20life%2C%20in%20particular%20in%20the%20political%20world.%20Fake%20news%20detection%20is%20an%20emerging%20research%20area%20which%20is%20gaining%20interest%20but%20involved%20some%20challenges%20due%20to%20the%20limited%20amount%20of%20resources%20%28i.e.%2C%20datasets%2C%20published%20literature%29%20available.%20We%20propose%20in%20this%20paper%2C%20a%20fake%20news%20detection%20model%20that%20use%20n-gram%20analysis%20and%20machine%20learning%20techniques.%20We%20investigate%20and%20compare%20two%20different%20features%20extraction%20techniques%20and%20six%20different%20machine%20classification%20techniques.%20Experimental%20evaluation%20yields%20the%20best%20performance%20using%20Term%20Frequency-Inverted%20Document%20Frequency%20%28TF-IDF%29%20as%20feature%20extraction%20technique%2C%20and%20Linear%20Support%20Vector%20Machine%20%28LSVM%29%20as%20a%20classifier%2C%20with%20an%20accuracy%20of%2092%25.%22%2C%22date%22%3A%222017%22%2C%22proceedingsTitle%22%3A%22Intelligent%2C%20Secure%2C%20and%20Dependable%20Systems%20in%20Distributed%20and%20Cloud%20Environments%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1007%5C%2F978-3-319-69155-8_9%22%2C%22ISBN%22%3A%22978-3-319-69155-8%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T08%3A09%3A54Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22Z5S8AXDE%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Potthast%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EPotthast%2C%20Martin%2C%20Johannes%20Kiesel%2C%20Kevin%20Reinartz%2C%20Janek%20Bevendorff%2C%20and%20Benno%20Stein.%20%26%23x201C%3BA%20Stylometric%20Inquiry%20into%20Hyperpartisan%20and%20Fake%20News.%26%23x201D%3B%20%3Ci%3EArXiv%3A1702.05638%20%5BCs%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.05638%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.05638%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DZ5S8AXDE%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20Stylometric%20Inquiry%20into%20Hyperpartisan%20and%20Fake%20News%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Martin%22%2C%22lastName%22%3A%22Potthast%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Johannes%22%2C%22lastName%22%3A%22Kiesel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Kevin%22%2C%22lastName%22%3A%22Reinartz%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Janek%22%2C%22lastName%22%3A%22Bevendorff%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Benno%22%2C%22lastName%22%3A%22Stein%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20reports%20on%20a%20writing%20style%20analysis%20of%20hyperpartisan%20%28i.e.%2C%20extremely%20one-sided%29%20news%20in%20connection%20to%20fake%20news.%20It%20presents%20a%20large%20corpus%20of%201%2C627%20articles%20that%20were%20manually%20fact-checked%20by%20professional%20journalists%20from%20BuzzFeed.%20The%20articles%20originated%20from%209%20well-known%20political%20publishers%2C%203%20each%20from%20the%20mainstream%2C%20the%20hyperpartisan%20left-wing%2C%20and%20the%20hyperpartisan%20right-wing.%20In%20sum%2C%20the%20corpus%20contains%20299%20fake%20news%2C%2097%25%20of%20which%20originated%20from%20hyperpartisan%20publishers.%20We%20propose%20and%20demonstrate%20a%20new%20way%20of%20assessing%20style%20similarity%20between%20text%20categories%20via%20Unmasking---a%20meta-learning%20approach%20originally%20devised%20for%20authorship%20verification---%2C%20revealing%20that%20the%20style%20of%20left-wing%20and%20right-wing%20news%20have%20a%20lot%20more%20in%20common%20than%20any%20of%20the%20two%20have%20with%20the%20mainstream.%20Furthermore%2C%20we%20show%20that%20hyperpartisan%20news%20can%20be%20discriminated%20well%20by%20its%20style%20from%20the%20mainstream%20%28F1%3D0.78%29%2C%20as%20can%20be%20satire%20from%20both%20%28F1%3D0.81%29.%20Unsurprisingly%2C%20style-based%20fake%20news%20detection%20does%20not%20live%20up%20to%20scratch%20%28F1%3D0.46%29.%20Nevertheless%2C%20the%20former%20results%20are%20important%20to%20implement%20pre-screening%20for%20fake%20news%20detectors.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1702.05638%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A13%3A08Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22UDXE3NVP%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22P%5Cu00e9rez-Rosas%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EP%26%23xE9%3Brez-Rosas%2C%20Ver%26%23xF3%3Bnica%2C%20Bennett%20Kleinberg%2C%20Alexandra%20Lefevre%2C%20and%20Rada%20Mihalcea.%20%26%23x201C%3BAutomatic%20Detection%20of%20Fake%20News.%26%23x201D%3B%20%3Ci%3EArXiv%3A1708.07104%20%5BCs%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1708.07104%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1708.07104%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DUDXE3NVP%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Automatic%20Detection%20of%20Fake%20News%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ver%5Cu00f3nica%22%2C%22lastName%22%3A%22P%5Cu00e9rez-Rosas%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Bennett%22%2C%22lastName%22%3A%22Kleinberg%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Alexandra%22%2C%22lastName%22%3A%22Lefevre%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rada%22%2C%22lastName%22%3A%22Mihalcea%22%7D%5D%2C%22abstractNote%22%3A%22The%20proliferation%20of%20misleading%20information%20in%20everyday%20access%20media%20outlets%20such%20as%20social%20media%20feeds%2C%20news%20blogs%2C%20and%20online%20newspapers%20have%20made%20it%20challenging%20to%20identify%20trustworthy%20news%20sources%2C%20thus%20increasing%20the%20need%20for%20computational%20tools%20able%20to%20provide%20insights%20into%20the%20reliability%20of%20online%20content.%20In%20this%20paper%2C%20we%20focus%20on%20the%20automatic%20identification%20of%20fake%20content%20in%20online%20news.%20Our%20contribution%20is%20twofold.%20First%2C%20we%20introduce%20two%20novel%20datasets%20for%20the%20task%20of%20fake%20news%20detection%2C%20covering%20seven%20different%20news%20domains.%20We%20describe%20the%20collection%2C%20annotation%2C%20and%20validation%20process%20in%20detail%20and%20present%20several%20exploratory%20analysis%20on%20the%20identification%20of%20linguistic%20differences%20in%20fake%20and%20legitimate%20news%20content.%20Second%2C%20we%20conduct%20a%20set%20of%20learning%20experiments%20to%20build%20accurate%20fake%20news%20detectors.%20In%20addition%2C%20we%20provide%20comparative%20analyses%20of%20the%20automatic%20and%20manual%20identification%20of%20fake%20news.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1708.07104%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A11%3A49Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22TN35EMBP%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ruchansky%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ERuchansky%2C%20Natali%2C%20Sungyong%20Seo%2C%20and%20Yan%20Liu.%20%26%23x201C%3BCSI%3A%20A%20Hybrid%20Deep%20Model%20for%20Fake%20News%20Detection.%26%23x201D%3B%20In%20%3Ci%3EProceedings%20of%20the%202017%20ACM%20on%20Conference%20on%20Information%20and%20Knowledge%20Management%3C%5C%2Fi%3E%2C%20797%26%23x2013%3B806.%20CIKM%20%26%23x2019%3B17.%20Singapore%2C%20Singapore%3A%20Association%20for%20Computing%20Machinery%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3132847.3132877%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3132847.3132877%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DTN35EMBP%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22CSI%3A%20A%20Hybrid%20Deep%20Model%20for%20Fake%20News%20Detection%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Natali%22%2C%22lastName%22%3A%22Ruchansky%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sungyong%22%2C%22lastName%22%3A%22Seo%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yan%22%2C%22lastName%22%3A%22Liu%22%7D%5D%2C%22abstractNote%22%3A%22The%20topic%20of%20fake%20news%20has%20drawn%20attention%20both%20from%20the%20public%20and%20the%20academic%20communities.%20Such%20misinformation%20has%20the%20potential%20of%20affecting%20public%20opinion%2C%20providing%20an%20opportunity%20for%20malicious%20parties%20to%20manipulate%20the%20outcomes%20of%20public%20events%20such%20as%20elections.%20Because%20such%20high%20stakes%20are%20at%20play%2C%20automatically%20detecting%20fake%20news%20is%20an%20important%2C%20yet%20challenging%20problem%20that%20is%20not%20yet%20well%20understood.%20Nevertheless%2C%20there%20are%20three%20generally%20agreed%20upon%20characteristics%20of%20fake%20news%3A%20the%20text%20of%20an%20article%2C%20the%20user%20response%20it%20receives%2C%20and%20the%20source%20users%20promoting%20it.%20Existing%20work%20has%20largely%20focused%20on%20tailoring%20solutions%20to%20one%20particular%20characteristic%20which%20has%20limited%20their%20success%20and%20generality.%20In%20this%20work%2C%20we%20propose%20a%20model%20that%20combines%20all%20three%20characteristics%20for%20a%20more%20accurate%20and%20automated%20prediction.%20Specifically%2C%20we%20incorporate%20the%20behavior%20of%20both%20parties%2C%20users%20and%20articles%2C%20and%20the%20group%20behavior%20of%20users%20who%20propagate%20fake%20news.%20Motivated%20by%20the%20three%20characteristics%2C%20we%20propose%20a%20model%20called%20CSI%20which%20is%20composed%20of%20three%20modules%3A%20Capture%2C%20Score%2C%20and%20Integrate.%20The%20first%20module%20is%20based%20on%20the%20response%20and%20text%3B%20it%20uses%20a%20Recurrent%20Neural%20Network%20to%20capture%20the%20temporal%20pattern%20of%20user%20activity%20on%20a%20given%20article.%20The%20second%20module%20learns%20the%20source%20characteristic%20based%20on%20the%20behavior%20of%20users%2C%20and%20the%20two%20are%20integrated%20with%20the%20third%20module%20to%20classify%20an%20article%20as%20fake%20or%20not.%20Experimental%20analysis%20on%20real-world%20data%20demonstrates%20that%20CSI%20achieves%20higher%20accuracy%20than%20existing%20models%2C%20and%20extracts%20meaningful%20latent%20representations%20of%20both%20users%20and%20articles.%22%2C%22date%22%3A%222017%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%202017%20ACM%20on%20Conference%20on%20Information%20and%20Knowledge%20Management%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1145%5C%2F3132847.3132877%22%2C%22ISBN%22%3A%22978-1-4503-4918-5%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F3132847.3132877%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A08%3A45Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22P9D2I8BZ%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Wang%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EWang%2C%20William%20Yang.%20%26%23x201C%3B%26%23x2018%3BLiar%2C%20Liar%20Pants%20on%20Fire%26%23x2019%3B%3A%20A%20New%20Benchmark%20Dataset%20for%20Fake%20News%20Detection.%26%23x201D%3B%20%3Ci%3EArXiv%3A1705.00648%20%5BCs%5D%3C%5C%2Fi%3E%2C%202017.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1705.00648%27%3Ehttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1705.00648%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DP9D2I8BZ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22%5C%22Liar%2C%20Liar%20Pants%20on%20Fire%5C%22%3A%20A%20New%20Benchmark%20Dataset%20for%20Fake%20News%20Detection%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22William%20Yang%22%2C%22lastName%22%3A%22Wang%22%7D%5D%2C%22abstractNote%22%3A%22Automatic%20fake%20news%20detection%20is%20a%20challenging%20problem%20in%20deception%20detection%2C%20and%20it%20has%20tremendous%20real-world%20political%20and%20social%20impacts.%20However%2C%20statistical%20approaches%20to%20combating%20fake%20news%20has%20been%20dramatically%20limited%20by%20the%20lack%20of%20labeled%20benchmark%20datasets.%20In%20this%20paper%2C%20we%20present%20liar%3A%20a%20new%2C%20publicly%20available%20dataset%20for%20fake%20news%20detection.%20We%20collected%20a%20decade-long%2C%2012.8K%20manually%20labeled%20short%20statements%20in%20various%20contexts%20from%20PolitiFact.com%2C%20which%20provides%20detailed%20analysis%20report%20and%20links%20to%20source%20documents%20for%20each%20case.%20This%20dataset%20can%20be%20used%20for%20fact-checking%20research%20as%20well.%20Notably%2C%20this%20new%20dataset%20is%20an%20order%20of%20magnitude%20larger%20than%20previously%20largest%20public%20fake%20news%20datasets%20of%20similar%20type.%20Empirically%2C%20we%20investigate%20automatic%20fake%20news%20detection%20based%20on%20surface-level%20linguistic%20patterns.%20We%20have%20designed%20a%20novel%2C%20hybrid%20convolutional%20neural%20network%20to%20integrate%20meta-data%20with%20text.%20We%20show%20that%20this%20hybrid%20approach%20can%20improve%20a%20text-only%20deep%20learning%20model.%22%2C%22date%22%3A%222017%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F1705.00648%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A06%3A50Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%2272Q8YIBR%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Paul%22%2C%22parsedDate%22%3A%222016%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EPaul%2C%20Michael%20J.%20%26%23x201C%3BInterpretable%20Machine%20Learning%3A%20Lessons%20from%20Topic%20Modeling.%26%23x201D%3B%20In%20%3Ci%3ECHI%20Workshop%20on%20Human-Centered%20Machine%20Learning%3C%5C%2Fi%3E%2C%202016.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fcmci.colorado.edu%5C%2F~mpaul%5C%2Ffiles%5C%2Fchi16hcml_interpretable.pdf%27%3Ehttps%3A%5C%2F%5C%2Fcmci.colorado.edu%5C%2F~mpaul%5C%2Ffiles%5C%2Fchi16hcml_interpretable.pdf%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D72Q8YIBR%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Interpretable%20Machine%20Learning%3A%20Lessons%20from%20Topic%20Modeling%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%20J.%22%2C%22lastName%22%3A%22Paul%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20examines%20how%20the%20topic%20modeling%20community%20has%20characterized%20interpretability%2C%20and%20discusses%20how%20ideas%20used%20in%20topic%20modeling%20could%20be%20used%20to%20make%20other%20types%20of%20machine%20learning%20more%20interpretable.%20Interpretability%20is%20discussed%20both%20from%20the%20perspective%20of%20evaluation%20%28%5Cu201chow%20interpretable%20is%20this%20model%3F%5Cu201d%29%20and%20training%20%28%5Cu201chow%20can%20we%20make%20this%20model%20more%20interpretable%3F%5Cu201d%29%20in%20machine%20learning.%22%2C%22date%22%3A%222016%22%2C%22proceedingsTitle%22%3A%22CHI%20Workshop%20on%20Human-Centered%20Machine%20Learning%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fcmci.colorado.edu%5C%2F~mpaul%5C%2Ffiles%5C%2Fchi16hcml_interpretable.pdf%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-08-18T18%3A25%3A20Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20interpretation%22%7D%2C%7B%22tag%22%3A%22Topic%20modeling%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22FGCXV2ID%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Conroy%20et%20al.%22%2C%22parsedDate%22%3A%222015%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EConroy%2C%20Niall%20J.%2C%20Victoria%20L.%20Rubin%2C%20and%20Yimin%20Chen.%20%26%23x201C%3BAutomatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News%3A%20Automatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News.%26%23x201D%3B%20%3Ci%3EProceedings%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%3C%5C%2Fi%3E%2052%2C%20no.%201%20%282015%29%3A%201%26%23x2013%3B4.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fpra2.2015.145052010082%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fpra2.2015.145052010082%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DFGCXV2ID%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Automatic%20deception%20detection%3A%20Methods%20for%20finding%20fake%20news%3A%20Automatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Niall%20J.%22%2C%22lastName%22%3A%22Conroy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Victoria%20L.%22%2C%22lastName%22%3A%22Rubin%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yimin%22%2C%22lastName%22%3A%22Chen%22%7D%5D%2C%22abstractNote%22%3A%22This%20research%20surveys%20the%20current%20state%5Cu2010of%5Cu2010the%5Cu2010art%20technologies%20that%20are%20instrumental%20in%20the%20adoption%20and%20development%20of%20fake%20news%20detection.%20%5Cu201cFake%20news%20detection%5Cu201d%20is%20defined%20as%20the%20task%20of%20categorizing%20news%20along%20a%20continuum%20of%20veracity%2C%20with%20an%20associated%20measure%20of%20certainty.%20Veracity%20is%20compromised%20by%20the%20occurrence%20of%20intentional%20deceptions.%20The%20nature%20of%20online%20news%20publication%20has%20changed%2C%20such%20that%20traditional%20fact%20checking%20and%20vetting%20from%20potential%20deception%20is%20impossible%20against%20the%20flood%20arising%20from%20content%20generators%2C%20as%20well%20as%20various%20formats%20and%20genres.%5Cn%5CnThe%20paper%20provides%20a%20typology%20of%20several%20varieties%20of%20veracity%20assessment%20methods%20emerging%20from%20two%20major%20categories%20%5Cu2013%20linguistic%20cue%20approaches%20%28with%20machine%20learning%29%2C%20and%20network%20analysis%20approaches.%20We%20see%20promise%20in%20an%20innovative%20hybrid%20approach%20that%20combines%20linguistic%20cue%20and%20machine%20learning%2C%20with%20network%5Cu2010based%20behavioral%20data.%20Although%20designing%20a%20fake%20news%20detector%20is%20not%20a%20straightforward%20problem%2C%20we%20propose%20operational%20guidelines%20for%20a%20feasible%20fake%20news%20detecting%20system.%22%2C%22date%22%3A%222015%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fpra2.2015.145052010082%22%2C%22ISSN%22%3A%2223739231%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fdoi.wiley.com%5C%2F10.1002%5C%2Fpra2.2015.145052010082%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A05%3A13Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Network%20analysis%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22QBG6SEPZ%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Dobson%22%2C%22parsedDate%22%3A%222015%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EDobson%2C%20James%20E.%20%26%23x201C%3BCan%20An%20Algorithm%20Be%20Disturbed%3F%3A%20Machine%20Learning%2C%20Intrinsic%20Criticism%2C%20and%20the%20Digital%20Humanities.%26%23x201D%3B%20%3Ci%3ECollege%20Literature%3C%5C%2Fi%3E%2042%2C%20no.%204%20%282015%29%3A%20543%26%23x2013%3B64.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fmuse.jhu.edu%5C%2Farticle%5C%2F595031%27%3Ehttps%3A%5C%2F%5C%2Fmuse.jhu.edu%5C%2Farticle%5C%2F595031%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DQBG6SEPZ%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Can%20An%20Algorithm%20Be%20Disturbed%3F%3A%20Machine%20Learning%2C%20Intrinsic%20Criticism%2C%20and%20the%20Digital%20Humanities%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22James%20E.%22%2C%22lastName%22%3A%22Dobson%22%7D%5D%2C%22abstractNote%22%3A%22This%20essay%20positions%20the%20use%20of%20machine%20learning%20within%20the%20digital%20humanities%20as%20part%20of%20a%20wider%20movement%20that%20nostalgically%20seeks%20to%20return%20literary%20criticism%20to%20the%20structuralist%20era%2C%20to%20a%20moment%20characterized%20by%20belief%20in%20systems%2C%20structure%2C%20and%20the%20transparency%20of%20language.%20It%20argues%20that%20the%20scientific%20criticism%20of%20the%20present%20attempts%20to%20separate%20methodology%20from%20interpretation%20and%20in%20the%20process%20it%20has%20deemphasized%20the%20degree%20to%20which%20methodology%20also%20participates%20in%20interpretation.%20This%20essay%20returns%20to%20the%20deconstructive%20critique%20of%20structuralism%20in%20order%20to%20highlight%20the%20ways%20in%20which%20numerous%20interpretive%20decisions%20are%20suppressed%20in%20the%20pre-processing%20of%20text%20and%20in%20the%20use%20of%20machine%20learning%20algorithms.%22%2C%22date%22%3A%222015%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%221542-4286%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fmuse.jhu.edu%5C%2Farticle%5C%2F595031%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-30T02%3A37%3A18Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22DH%20Digital%20humanities%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%2245LNNE27%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Burscher%20et%20al.%22%2C%22parsedDate%22%3A%222014%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EBurscher%2C%20Bj%26%23xF6%3Brn%2C%20Daan%20Odijk%2C%20Rens%20Vliegenthart%2C%20Maarten%20de%20Rijke%2C%20and%20Claes%20H.%20de%20Vreese.%20%26%23x201C%3BTeaching%20the%20Computer%20to%20Code%20Frames%20in%20News%3A%20Comparing%20Two%20Supervised%20Machine%20Learning%20Approaches%20to%20Frame%20Analysis.%26%23x201D%3B%20%3Ci%3ECommunication%20Methods%20and%20Measures%3C%5C%2Fi%3E%208%2C%20no.%203%20%282014%29%3A%20190%26%23x2013%3B206.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1080%5C%2F19312458.2014.937527%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1080%5C%2F19312458.2014.937527%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D45LNNE27%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Teaching%20the%20Computer%20to%20Code%20Frames%20in%20News%3A%20Comparing%20Two%20Supervised%20Machine%20Learning%20Approaches%20to%20Frame%20Analysis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Bj%5Cu00f6rn%22%2C%22lastName%22%3A%22Burscher%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Daan%22%2C%22lastName%22%3A%22Odijk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rens%22%2C%22lastName%22%3A%22Vliegenthart%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Maarten%20de%22%2C%22lastName%22%3A%22Rijke%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Claes%20H.%20de%22%2C%22lastName%22%3A%22Vreese%22%7D%5D%2C%22abstractNote%22%3A%22We%20explore%20the%20application%20of%20supervised%20machine%20learning%20%28SML%29%20to%20frame%20coding.%20By%20automating%20the%20coding%20of%20frames%20in%20news%2C%20SML%20facilitates%20the%20incorporation%20of%20large-scale%20content%20analysis%20into%20framing%20research%2C%20even%20if%20financial%20resources%20are%20scarce.%20This%20furthers%20a%20more%20integrated%20investigation%20of%20framing%20processes%20conceptually%20as%20well%20as%20methodologically.%20We%20conduct%20several%20experiments%20in%20which%20we%20automate%20the%20coding%20of%20four%20generic%20frames%20that%20are%20operationalised%20as%20a%20set%20of%20indicator%20questions.%20In%20doing%20so%2C%20we%20compare%20two%20approaches%20to%20modelling%20the%20coherence%20between%20indicator%20questions%20and%20frames%20as%20an%20SML%20task.%20The%20results%20of%20our%20experiments%20show%20that%20SML%20is%20well%20suited%20to%20automate%20frame%20coding%20but%20that%20coding%20performance%20is%20dependent%20on%20the%20way%20SML%20is%20implemented.%22%2C%22date%22%3A%222014%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1080%5C%2F19312458.2014.937527%22%2C%22ISSN%22%3A%221931-2458%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1080%5C%2F19312458.2014.937527%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-08-15T22%3A28%3A57Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Frame%20analysis%20of%20media%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%229C54BR32%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Freitas%22%2C%22parsedDate%22%3A%222014%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EFreitas%2C%20Alex%20A.%20%26%23x201C%3BComprehensible%20Classification%20Models%3A%20A%20Position%20Paper.%26%23x201D%3B%20In%20%3Ci%3EACM%20SIGKDD%20Explorations%3C%5C%2Fi%3E%2C%2015.1%3A1%26%23x2013%3B10.%20Association%20for%20Computing%20Machinery%2C%202014.%20%3Ca%20class%3D%27zp-ItemURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F2594473.2594475%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F2594473.2594475%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3D9C54BR32%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Comprehensible%20classification%20models%3A%20a%20position%20paper%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Alex%20A.%22%2C%22lastName%22%3A%22Freitas%22%7D%5D%2C%22abstractNote%22%3A%22The%20vast%20majority%20of%20the%20literature%20evaluates%20the%20performance%20of%20classification%20models%20using%20only%20the%20criterion%20of%20predictive%20accuracy.%20This%20paper%20reviews%20the%20case%20for%20considering%20also%20the%20comprehensibility%20%28interpretability%29%20of%20classification%20models%2C%20and%20discusses%20the%20interpretability%20of%20five%20types%20of%20classification%20models%2C%20namely%20decision%20trees%2C%20classification%20rules%2C%20decision%20tables%2C%20nearest%20neighbors%20and%20Bayesian%20network%20classifiers.%20We%20discuss%20both%20interpretability%20issues%20which%20are%20specific%20to%20each%20of%20those%20model%20types%20and%20more%20generic%20interpretability%20issues%2C%20namely%20the%20drawbacks%20of%20using%20model%20size%20as%20the%20only%20criterion%20to%20evaluate%20the%20comprehensibility%20of%20a%20model%2C%20and%20the%20use%20of%20monotonicity%20constraints%20to%20improve%20the%20comprehensibility%20and%20acceptance%20of%20classification%20models%20by%20users.%22%2C%22date%22%3A%222014%22%2C%22proceedingsTitle%22%3A%22ACM%20SIGKDD%20Explorations%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F2594473.2594475%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-08-12T19%3A14%3A01Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Text%20classification%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22BNGLJNZU%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Grimmer%20and%20King%22%2C%22parsedDate%22%3A%222011%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3EGrimmer%2C%20Justin%2C%20and%20Gary%20King.%20%26%23x201C%3BGeneral%20Purpose%20Computer-Assisted%20Clustering%20and%20Conceptualization.%26%23x201D%3B%20%3Ci%3EProceedings%20of%20the%20National%20Academy%20of%20Sciences%3C%5C%2Fi%3E%20108%2C%20no.%207%20%282011%29%3A%202643%26%23x2013%3B50.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1073%5C%2Fpnas.1018067108%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1073%5C%2Fpnas.1018067108%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DBNGLJNZU%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22General%20purpose%20computer-assisted%20clustering%20and%20conceptualization%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Justin%22%2C%22lastName%22%3A%22Grimmer%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gary%22%2C%22lastName%22%3A%22King%22%7D%5D%2C%22abstractNote%22%3A%22%5BFirst%20paragraph%20of%20abstract%5D%3A%20We%20develop%20a%20computer-assisted%20method%20for%20the%20discovery%20of%20insightful%20conceptualizations%2C%20in%20the%20form%20of%20clusterings%20%28i.e.%2C%20partitions%29%20of%20input%20objects.%20Each%20of%20the%20numerous%20fully%20automated%20methods%20of%20cluster%20analysis%20proposed%20in%20statistics%2C%20computer%20science%2C%20and%20biology%20optimize%20a%20different%20objective%20function.%20Almost%20all%20are%20well%20defined%2C%20but%20how%20to%20determine%20before%20the%20fact%20which%20one%2C%20if%20any%2C%20will%20partition%20a%20given%20set%20of%20objects%20in%20an%20%5Cu201cinsightful%5Cu201d%20or%20%5Cu201cuseful%5Cu201d%20way%20for%20a%20given%20user%20is%20unknown%20and%20difficult%2C%20if%20not%20logically%20impossible.%20We%20develop%20a%20metric%20space%20of%20partitions%20from%20all%20existing%20cluster%20analysis%20methods%20applied%20to%20a%20given%20dataset%20%28along%20with%20millions%20of%20other%20solutions%20we%20add%20based%20on%20combinations%20of%20existing%20clusterings%29%20and%20enable%20a%20user%20to%20explore%20and%20interact%20with%20it%20and%20quickly%20reveal%20or%20prompt%20useful%20or%20insightful%20conceptualizations.%20In%20addition%2C%20although%20it%20is%20uncommon%20to%20do%20so%20in%20unsupervised%20learning%20problems%2C%20we%20offer%20and%20implement%20evaluation%20designs%20that%20make%20our%20computer-assisted%20approach%20vulnerable%20to%20being%20proven%20suboptimal%20in%20specific%20data%20types.%20We%20demonstrate%20that%20our%20approach%20facilitates%20more%20efficient%20and%20insightful%20discovery%20of%20useful%20information%20than%20expert%20human%20coders%20or%20many%20existing%20fully%20automated%20methods.%22%2C%22date%22%3A%222011%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1073%5C%2Fpnas.1018067108%22%2C%22ISSN%22%3A%220027-8424%2C%201091-6490%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.pnas.org%5C%2Fcontent%5C%2F108%5C%2F7%5C%2F2643%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-08-18T20%3A27%3A18Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Topic%20clusters%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22FQPMGJA6%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Sculley%20and%20Pasanek%22%2C%22parsedDate%22%3A%222008%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESculley%2C%20D.%2C%20and%20B.%20M.%20Pasanek.%20%26%23x201C%3BMeaning%20and%20Mining%3A%20The%20Impact%20of%20Implicit%20Assumptions%20in%20Data%20Mining%20for%20the%20Humanities.%26%23x201D%3B%20%3Ci%3ELiterary%20and%20Linguistic%20Computing%3C%5C%2Fi%3E%2023%2C%20no.%204%20%282008%29%3A%20409%26%23x2013%3B24.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1093%5C%2Fllc%5C%2Ffqn019%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1093%5C%2Fllc%5C%2Ffqn019%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DFQPMGJA6%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Meaning%20and%20mining%3A%20the%20impact%20of%20implicit%20assumptions%20in%20data%20mining%20for%20the%20humanities%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22D.%22%2C%22lastName%22%3A%22Sculley%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22B.%20M.%22%2C%22lastName%22%3A%22Pasanek%22%7D%5D%2C%22abstractNote%22%3A%22This%20article%20makes%20explicit%20some%20of%20the%20foundational%20assumptions%20of%20machine%20learning%20methods%2C%20and%20presents%20a%20series%20of%20experiments%20as%20a%20case%20study%20and%20object%20lesson%20in%20the%20potential%20pitfalls%20in%20the%20use%20of%20data%20mining%20methods%20for%20hypothesis%20testing%20in%20literary%20scholarship.%20The%20worst%20dangers%20may%20lie%20in%20the%20humanist%27s%20ability%20to%20interpret%20nearly%20any%20result%2C%20projecting%20his%20or%20her%20own%20biases%20into%20the%20outcome%20of%20an%20experiment%5Cu2014perhaps%20all%20the%20more%20unwittingly%20due%20to%20the%20superficial%20objectivity%20of%20computational%20methods.%20The%20authors%20argue%20that%20in%20the%20digital%20humanities%2C%20the%20standards%20for%20the%20initial%20production%20of%20evidence%20should%20be%20even%20more%20rigorous%20than%20in%20the%20empirical%20sciences%20because%20of%20the%20subjective%20nature%20of%20the%20work%20that%20follows.%20Thus%2C%20they%20conclude%20with%20a%20discussion%20of%20recommended%20best%20practices%20for%20making%20results%20from%20data%20mining%20in%20the%20humanities%20domain%20as%20meaningful%20as%20possible.%20These%20include%20methods%20for%20keeping%20the%20boundary%20between%20computational%20results%20and%20subsequent%20interpretation%20as%20clearly%20delineated%20as%20possible.%22%2C%22date%22%3A%222008%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1093%5C%2Fllc%5C%2Ffqn019%22%2C%22ISSN%22%3A%220268-1145%2C%201477-4615%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Facademic.oup.com%5C%2Fdsh%5C%2Farticle-lookup%5C%2Fdoi%5C%2F10.1093%5C%2Fllc%5C%2Ffqn019%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A43%3A42Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22DH%20Digital%20humanities%22%7D%2C%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22IC8UJ7SR%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sebastiani%22%2C%22parsedDate%22%3A%222002%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%3Cdiv%20class%3D%5C%22csl-bib-body%5C%22%20style%3D%5C%22line-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%5C%22%3E%5Cn%20%20%3Cdiv%20class%3D%5C%22csl-entry%5C%22%3ESebastiani%2C%20Fabrizio.%20%26%23x201C%3BMachine%20Learning%20in%20Automated%20Text%20Categorization.%26%23x201D%3B%20%3Ci%3EACM%20Computing%20Surveys%20%28CSUR%29%3C%5C%2Fi%3E%2034%2C%20no.%201%20%282002%29%3A%201%26%23x2013%3B47.%20%3Ca%20class%3D%27zp-DOIURL%27%20href%3D%27https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F505282.505283%27%3Ehttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1145%5C%2F505282.505283%3C%5C%2Fa%3E.%20%3Ca%20title%3D%27Cite%20in%20RIS%20Format%27%20class%3D%27zp-CiteRIS%27%20href%3D%27https%3A%5C%2F%5C%2Fwe1s.ucsb.edu%5C%2Fwp-content%5C%2Fplugins%5C%2Fzotpress%5C%2Flib%5C%2Frequest%5C%2Frequest.cite.php%3Fapi_user_id%3D2133649%26amp%3Bitem_key%3DIC8UJ7SR%27%3ECite%3C%5C%2Fa%3E%20%3C%5C%2Fdiv%3E%5Cn%3C%5C%2Fdiv%3E%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Machine%20learning%20in%20automated%20text%20categorization%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Fabrizio%22%2C%22lastName%22%3A%22Sebastiani%22%7D%5D%2C%22abstractNote%22%3A%22The%20automated%20categorization%20%28or%20classification%29%20of%20texts%20into%20predefined%20categories%20has%20witnessed%20a%20booming%20interest%20in%20the%20last%2010%20years%2C%20due%20to%20the%20increased%20availability%20of%20documents%20in%20digital%20form%20and%20the%20ensuing%20need%20to%20organize%20them.%20In%20the%20research%20community%20the%20dominant%20approach%20to%20this%20problem%20is%20based%20on%20machine%20learning%20techniques%3A%20a%20general%20inductive%20process%20automatically%20builds%20a%20classifier%20by%20learning%2C%20from%20a%20set%20of%20preclassified%20documents%2C%20the%20characteristics%20of%20the%20categories.%20The%20advantages%20of%20this%20approach%20over%20the%20knowledge%20engineering%20approach%20%28consisting%20in%20the%20manual%20definition%20of%20a%20classifier%20by%20domain%20experts%29%20are%20a%20very%20good%20effectiveness%2C%20considerable%20savings%20in%20terms%20of%20expert%20labor%20power%2C%20and%20straightforward%20portability%20to%20different%20domains.%20This%20survey%20discusses%20the%20main%20approaches%20to%20text%20categorization%20that%20fall%20within%20the%20machine%20learning%20paradigm.%20We%20will%20discuss%20in%20detail%20issues%20pertaining%20to%20three%20different%20problems%2C%20namely%2C%20document%20representation%2C%20classifier%20construction%2C%20and%20classifier%20evaluation.%22%2C%22date%22%3A%222002%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1145%5C%2F505282.505283%22%2C%22ISSN%22%3A%220360-0300%2C%201557-7341%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fdl.acm.org%5C%2Fdoi%5C%2F10.1145%5C%2F505282.505283%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-07-25T19%3A49%3A12Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Text%20Analysis%22%7D%2C%7B%22tag%22%3A%22Text%20classification%22%7D%5D%7D%7D%5D%7D
Shadrova, Anna. “Topic Models Do Not Model Topics: Epistemological Remarks and Steps towards Best Practices.” Journal of Data Mining & Digital Humanities 2021 (2021). https://doi.org/10.46298/jdmdh.7595. Cite
Smith, Gary, and Jay Cordes. The Phantom Pattern Problem: The Mirage of Big Data. First edition. Oxford ; New York, NY: Oxford University Press, 2020. Cite
Heaven, Will Douglass. “AI Is Wrestling with a Replication Crisis.” MIT Technology Review, 2020. https://www.technologyreview.com/2020/11/12/1011944/artificial-intelligence-replication-crisis-science-big-tech-google-deepmind-facebook-openai/. Cite
Kwak, Haewoon, Jisun An, and Yong-Yeol Ahn. “A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017.” ArXiv:2005.01803 [Cs], 2020. http://arxiv.org/abs/2005.01803. Cite
Dickson, Ben. “The Advantages of Self-Explainable AI over Interpretable AI.” The Next Web, 2020. https://thenextweb.com/neural/2020/06/19/the-advantages-of-self-explainable-ai-over-interpretable-ai/. Cite
Rogers, Anna, Olga Kovaleva, and Anna Rumshisky. “A Primer in BERTology: What We Know about How BERT Works.” ArXiv:2002.12327 [Cs], 2020. http://arxiv.org/abs/2002.12327. Cite
Munro, Robert. Human-in-the-Loop Machine Learning. Shelter Island, New York: Manning, 2020. https://www.manning.com/books/human-in-the-loop-machine-learning. Cite
Selbst, Andrew D., Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. “Fairness and Abstraction in Sociotechnical Systems.” In Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. FAT* ’19. Atlanta, GA, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3287560.3287598. Cite
Rudin, Cynthia. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence 1, no. 5 (2019): 206–15. https://doi.org/10.1038/s42256-019-0048-x. Cite
Molnar, Christoph. Interpretable Machine Learning. Christoph Molnar, 2019. https://christophm.github.io/interpretable-ml-book/. Cite
Murdoch, W. James, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. “Interpretable Machine Learning: Definitions, Methods, and Applications.” ArXiv:1901.04592 [Cs, Stat], 2019. http://arxiv.org/abs/1901.04592. Cite
Shu, Kai, Suhang Wang, and Huan Liu. “Beyond News Contents: The Role of Social Context for Fake News Detection.” In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 312–20. WSDM ’19. Melbourne VIC, Australia: Association for Computing Machinery, 2019. https://doi.org/10.1145/3289600.3290994. Cite
“Milestones:DIALOG Online Search System, 1966 - Engineering and Technology History Wiki,” 2019. https://ethw.org/Milestones:DIALOG_Online_Search_System,_1966. Cite
Narayanan, Menaka, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. “How Do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation.” ArXiv:1802.00682 [Cs], 2018. http://arxiv.org/abs/1802.00682. Cite
Selbst, Andrew D., and Solon Barocas. “The Intuitive Appeal of Explainable Machines.” SSRN Electronic Journal, 2018. https://doi.org/10.2139/ssrn.3126971. Cite
Hind, Michael, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. “TED: Teaching AI to Explain Its Decisions.” ArXiv:1811.04896 [Cs], 2018. http://arxiv.org/abs/1811.04896. Cite
Alvarez-Melis, David, and Tommi Jaakkola. “Towards Robust Interpretability with Self-Explaining Neural Networks.” In Advances in Neural Information Processing Systems 31, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, 7775–84. Curran Associates, Inc., 2018. http://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining-neural-networks.pdf. Cite
Gall, Richard. Machine Learning Explainability vs Interpretability: Two Concepts That Could Help Restore Trust in AI, 2018. https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html. Cite
Gilpin, Leilani H., David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. “Explaining Explanations: An Overview of Interpretability of Machine Learning.” ArXiv:1806.00069 [Cs, Stat], 2018. http://arxiv.org/abs/1806.00069. Cite
Spencer, Ann. Make Machine Learning Interpretability More Rigorous, 2018. https://blog.dominodatalab.com/make-machine-learning-interpretability-rigorous/. Cite
Gill, Patrick Hall. Navdeep. Introduction to Machine Learning Interpretability. S.l.: O’Reilly Media, Inc., 2018. https://proquest.safaribooksonline.com/9781492033158. Cite
Parikh, Shivam B., and Pradeep K. Atrey. “Media-Rich Fake News Detection: A Survey.” In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 436–41, 2018. https://doi.org/10.1109/MIPR.2018.00093. Cite
Wu, Liang, and Huan Liu. “Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate.” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 637–45. WSDM ’18. Marina Del Rey, CA, USA: Association for Computing Machinery, 2018. https://doi.org/10.1145/3159652.3159677. Cite
Lipton, Zachary, and Jacob Steinhardt. Troubling Trends in Machine Learning Scholarship.Pdf, 2018. https://www.dropbox.com/s/ao7c090p8bg1hk3/Lipton%20and%20Steinhardt%20-%20Troubling%20Trends%20in%20Machine%20Learning%20Scholarship.pdf?dl=0. Cite
Randles, Bernadette M., Irene V. Pasquetto, Milena S. Golshan, and Christine L. Borgman. “Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study.” In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 1–2, 2017. https://doi.org/10.1109/JCDL.2017.7991618. Cite
Lipton, Zachary C. “The Mythos of Model Interpretability.” ArXiv:1606.03490 [Cs, Stat], 2017. http://arxiv.org/abs/1606.03490. Cite
Edwards, Lilian, and Michael Veale. “Slave to the Algorithm? Why a ‘Right to an Explanation’ Is Probably Not the Remedy You Are Looking For.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 2017. https://papers.ssrn.com/abstract=2972855. Cite
Samek, Wojciech, Thomas Wiegand, and Klaus-Robert Müller. “Explainable Artificial Intelligence.” International Telecommunication Union Journal, no. 1 (2017): 1–10. https://www.itu.int/en/journal/001/Pages/05.aspx. Cite
Doshi-Velez, Finale, and Been Kim. “Towards A Rigorous Science of Interpretable Machine Learning.” ArXiv:1702.08608 [Cs, Stat], 2017. http://arxiv.org/abs/1702.08608. Cite
Shu, Kai, Suhang Wang, and Huan Liu. “Exploiting Tri-Relationship for Fake News Detection.” undefined, 2017. https://www.semanticscholar.org/paper/Exploiting-Tri-Relationship-for-Fake-News-Detection-Shu-Wang/8fd1d13e18c5ef8b57296adab6543cb810c36d81. Cite
Granik, Mykhailo, and Volodymyr Mesyura. “Fake News Detection Using Naive Bayes Classifier.” In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 900–903, 2017. https://doi.org/10.1109/UKRCON.2017.8100379. Cite
Tacchini, Eugenio, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, and Luca de Alfaro. “Some Like It Hoax: Automated Fake News Detection in Social Networks.” ArXiv:1704.07506 [Cs], 2017. http://arxiv.org/abs/1704.07506. Cite
Ahmed, Hadeer, Issa Traore, and Sherif Saad. “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques.” In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, edited by Issa Traore, Isaac Woungang, and Ahmed Awad, 127–38. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-69155-8_9. Cite
Potthast, Martin, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. “A Stylometric Inquiry into Hyperpartisan and Fake News.” ArXiv:1702.05638 [Cs], 2017. http://arxiv.org/abs/1702.05638. Cite
Pérez-Rosas, Verónica, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. “Automatic Detection of Fake News.” ArXiv:1708.07104 [Cs], 2017. http://arxiv.org/abs/1708.07104. Cite
Ruchansky, Natali, Sungyong Seo, and Yan Liu. “CSI: A Hybrid Deep Model for Fake News Detection.” In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 797–806. CIKM ’17. Singapore, Singapore: Association for Computing Machinery, 2017. https://doi.org/10.1145/3132847.3132877. Cite
Wang, William Yang. “‘Liar, Liar Pants on Fire’: A New Benchmark Dataset for Fake News Detection.” ArXiv:1705.00648 [Cs], 2017. http://arxiv.org/abs/1705.00648. Cite
Paul, Michael J. “Interpretable Machine Learning: Lessons from Topic Modeling.” In CHI Workshop on Human-Centered Machine Learning, 2016. https://cmci.colorado.edu/~mpaul/files/chi16hcml_interpretable.pdf. Cite
Conroy, Niall J., Victoria L. Rubin, and Yimin Chen. “Automatic Deception Detection: Methods for Finding Fake News: Automatic Deception Detection: Methods for Finding Fake News.” Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1–4. https://doi.org/10.1002/pra2.2015.145052010082. Cite
Dobson, James E. “Can An Algorithm Be Disturbed?: Machine Learning, Intrinsic Criticism, and the Digital Humanities.” College Literature 42, no. 4 (2015): 543–64. https://muse.jhu.edu/article/595031. Cite
Burscher, Björn, Daan Odijk, Rens Vliegenthart, Maarten de Rijke, and Claes H. de Vreese. “Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis.” Communication Methods and Measures 8, no. 3 (2014): 190–206. https://doi.org/10.1080/19312458.2014.937527. Cite
Freitas, Alex A. “Comprehensible Classification Models: A Position Paper.” In ACM SIGKDD Explorations, 15.1:1–10. Association for Computing Machinery, 2014. https://doi.org/10.1145/2594473.2594475. Cite
Grimmer, Justin, and Gary King. “General Purpose Computer-Assisted Clustering and Conceptualization.” Proceedings of the National Academy of Sciences 108, no. 7 (2011): 2643–50. https://doi.org/10.1073/pnas.1018067108. Cite
Sculley, D., and B. M. Pasanek. “Meaning and Mining: The Impact of Implicit Assumptions in Data Mining for the Humanities.” Literary and Linguistic Computing 23, no. 4 (2008): 409–24. https://doi.org/10.1093/llc/fqn019. Cite
Sebastiani, Fabrizio. “Machine Learning in Automated Text Categorization.” ACM Computing Surveys (CSUR) 34, no. 1 (2002): 1–47. https://doi.org/10.1145/505282.505283. Cite