{"id":572,"date":"2014-12-22T14:00:00","date_gmt":"2014-12-22T14:00:00","guid":{"rendered":"https:\/\/blogs.technet.microsoft.com\/inside_microsoft_research\/2014\/12\/22\/addressing-fairness-accountability-and-transparency-in-machine-learning\/"},"modified":"2019-07-22T13:19:31","modified_gmt":"2019-07-22T20:19:31","slug":"addressing-fairness-accountability-and-transparency-in-machine-learning","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/addressing-fairness-accountability-and-transparency-in-machine-learning\/","title":{"rendered":"Addressing Fairness, Accountability, and Transparency in Machine Learning"},"content":{"rendered":"<p class=\"posted-by\">Posted by <span class=\"author\">Microsoft Research<\/span><\/p>\n<p>Machine learning and big data are certainly hot topics that emerged within the tech community in 2014. But what are the real-world implications for how we interpret what happens inside the data centers that churn through mountains of seemingly endless data?<\/p>\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/research.microsoft.com\/en-us\/people\/wallach\/\" title=\"Hanna Wallach\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" src=\"https:\/\/msdnshared.blob.core.windows.net\/media\/TNBlogsFS\/prod.evol.blogs.technet.com\/CommunityServer.Blogs.Components.WeblogFiles\/00\/00\/00\/90\/35\/hanna-wallach-250.jpg\" alt=\"Hanna Wallach\" title=\"Hanna Wallach\" style=\"float:right;margin:5px\" \/><span class=\"sr-only\"> (opens in new tab)<\/span><\/a>For Microsoft machine learning researcher <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/research.microsoft.com\/en-us\/people\/wallach\/\" title=\"Hanna Wallach\" target=\"_blank\" rel=\"noopener noreferrer\">Hanna Wallach<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/x.com\/hannawallach\" title=\"Follow Hanna Wallach on Twitter\" target=\"_blank\" rel=\"noopener noreferrer\">@hannawallach<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>), opportunity lies outside the box. As an invited speaker at the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/fatml.org\/\" title=\"NIPS 2014 workshop on Fairness, Accountability, and Transparency in Machine Learning\" target=\"_blank\" rel=\"noopener noreferrer\">NIPS 2014 workshop on Fairness, Accountability, and Transparency in Machine Learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Wallach spoke about how her shift in research to the emerging field of computational social science led her to new insights about how <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/research.microsoft.com\/en-us\/about\/our-research\/machine-learning.aspx\" title=\"Machine Learning at Microsoft Research\">machine learning<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> methods can be applied to analyze real-world data about society.<\/p>\n<p>Her talk, \"<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/medium.com\/@hannawallach\/big-data-machine-learning-and-the-social-sciences-927a8e20460d\" title=\"Big Data, Machine Learning, and the Social Sciences\" target=\"_blank\" rel=\"noopener noreferrer\">Big Data, Machine Learning, and the Social Sciences,&rdquo; now available online<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, focuses on the four keys that she says lie at the heart of the matter: data, questions, models, and findings.<\/p>\n<p>\"Within computer science, there&rsquo;s a lot of enthusiasm about big data at the moment,\" Wallach says. \"But when it comes to addressing bias, fairness, and inclusion, perhaps we need to focus our attention on the granular nature of big data, or the fact that there may be many interesting data sets, nested within these larger collections, for which average-case statistical patterns may not hold.\"<\/p>\n<p>A&nbsp;researcher at <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" rel=\"noopener noreferrer\" target=\"_blank\" href=\"http:\/\/research.microsoft.com\/en-us\/labs\/newyork\/\" title=\"Microsoft Research New York City\">Microsoft Research New York City<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Wallach also is a core faculty member in the recently formed Computational Social Science Initiative at the University of Massachusetts Amherst.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Posted by Microsoft Research Machine learning and big data are certainly hot topics that emerged within the tech community in 2014. But what are the real-world implications for how we interpret what happens inside the data centers that churn through mountains of seemingly endless data? For Microsoft machine learning researcher Hanna Wallach (@hannawallach), opportunity lies [&hellip;]<\/p>\n","protected":false},"author":32627,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Hanna Wallach","user_id":"34779"}],"msr_hide_image_in_river":0,"footnotes":""},"categories":[1],"tags":[186831,201041,201799,186418,203063],"research-area":[],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-572","post","type-post","status-publish","format-standard","hentry","category-research-blog","tag-big-data","tag-computational-social-sciences","tag-hanna-wallach","tag-machine-learning","tag-nips-2014","msr-locale-en_us"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199571],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[372368],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Hanna Wallach","user_id":34779,"display_name":"Hanna Wallach","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wallach\/\" aria-label=\"Visit the profile page for Hanna Wallach\">Hanna Wallach<\/a>","is_active":false,"last_first":"Wallach, Hanna","people_section":0,"alias":"wallach"}],"msr_type":"Post","byline":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wallach\/\" title=\"Go to researcher profile for Hanna Wallach\" aria-label=\"Go to researcher profile for Hanna Wallach\" data-bi-type=\"byline author\" data-bi-cN=\"Hanna Wallach\">Hanna Wallach<\/a>","formattedDate":"December 22, 2014","formattedExcerpt":"Posted by Microsoft Research Machine learning and big data are certainly hot topics that emerged within the tech community in 2014. But what are the real-world implications for how we interpret what happens inside the data centers that churn through mountains of seemingly endless data?&hellip;","locale":{"slug":"en_us","name":"English","native":"","english":"English"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/572","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/32627"}],"replies":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/comments?post=572"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/572\/revisions"}],"predecessor-version":[{"id":599055,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/posts\/572\/revisions\/599055"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=572"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/categories?post=572"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/tags?post=572"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=572"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=572"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=572"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=572"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=572"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=572"},{"taxonomy":"msr-promo-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-promo-type?post=572"},{"taxonomy":"msr-podcast-series","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-podcast-series?post=572"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}