{"id":791531,"date":"2021-11-16T08:00:25","date_gmt":"2021-11-16T16:00:25","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=791531"},"modified":"2021-11-16T12:40:01","modified_gmt":"2021-11-16T20:40:01","slug":"panel-discussion-maximizing-benefits-and-minimizing-harms-with-language-technologies","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/panel-discussion-maximizing-benefits-and-minimizing-harms-with-language-technologies\/","title":{"rendered":"Panel: Maximizing benefits and minimizing harms with language technologies"},"content":{"rendered":"<p>Language is one of the main ways in which people understand and construct the social world. Current language technologies can contribute positively to this process\u2014by challenging existing power dynamics, or negatively\u2014by reproducing or exacerbating existing social inequities. In this panel, we will discuss existing concerns and opportunities related to the fairness, accountability, transparency, and ethics (FATE) of language technologies and the data they ingest or generate. It\u2019s important to address these matters because language technologies might surface, replicate, exacerbate or even cause a range of computational harms\u2014from exposing offensive speech or reinforcing stereotypes, to even more subtle issues, like nudging users towards undesirable patterns of behavior or triggering memories of traumatic events. In this session, we\u2019ll cover such critical questions as: How can we reliably measure fairness-related and other computational harms? Whose data is included in training a model, and who is excluded as a result? How do we better foresee potential computational harms from language technologies?<\/p>\n<p>Learn more about the 2021 Microsoft Research Summit: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/Aka.ms\/researchsummit\">https:\/\/Aka.ms\/researchsummit<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Language is one of the main ways in which people understand and construct the social world. Current language technologies can contribute positively to this process\u2014by challenging existing power dynamics, or negatively\u2014by reproducing or exacerbating existing social inequities. In this panel, we will discuss existing concerns and opportunities related to the fairness, accountability, transparency, and ethics [&hellip;]<\/p>\n","protected":false},"featured_media":791534,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13556,13545],"msr-video-type":[261263,261278],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-791531","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-video-type-research-summit-2021","msr-video-type-responsible-ai","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/x58gXO0vMTI","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/791531"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/791531\/revisions"}],"predecessor-version":[{"id":796675,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/791531\/revisions\/796675"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/791534"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=791531"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=791531"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=791531"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=791531"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=791531"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=791531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}