{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:02:41Z","timestamp":1782860561709,"version":"3.54.5"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"125","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGACCESS Access. Comput."],"published-print":{"date-parts":[[2020,3,2]]},"abstract":"<jats:p>AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.<\/jats:p>","DOI":"10.1145\/3386296.3386298","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T20:51:31Z","timestamp":1583182291000},"page":"1-1","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Toward fairness in AI for people with disabilities SBG@a research roadmap"],"prefix":"10.1145","author":[{"given":"Anhong","family":"Guo","sequence":"first","affiliation":[{"name":"Microsoft Research, Redmond, WA &amp; New York, NY and Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ece","family":"Kamar","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA &amp; New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer Wortman","family":"Vaughan","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA &amp; New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Wallach","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA &amp; New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meredith Ringel","family":"Morris","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA &amp; New York, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"A reductions approach to fair classification. arXiv preprint arXiv:1803.02453","author":"Agarwal Alekh","year":"2018","unstructured":"Alekh Agarwal, Alina Beygelzimer, Miroslav Dud\u00edk, John Langford, and Hanna Wallach. 2018. 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