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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13554,13553],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-600966","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-10-27","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"ACM","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/07\/Research_Roadmap_ASSETS_2019_Workshop_final.pdf","id":"601422","title":"research_roadmap_assets_2019_workshop_final","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":601422,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/08\/Research_Roadmap_ASSETS_2019_Workshop_final.pdf"},{"id":600972,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/07\/AI_Fairness_and_Disability_Research_Roadmap_ASSETS_2019_Workshop.pdf"}],"msr-author-ordering":[{"type":"text","value":"Anhong Guo","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Ece Kamar","user_id":31710,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ece Kamar"},{"type":"edited_text","value":"Jennifer Wortman Vaughan","user_id":32235,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jennifer Wortman Vaughan"},{"type":"user_nicename","value":"Hanna Wallach","user_id":34779,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Hanna Wallach"},{"type":"user_nicename","value":"Meredith Ringel Morris","user_id":32884,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Meredith Ringel Morris"}],"msr_impact_theme":[],"msr_research_lab":[199565,199571],"msr_event":[],"msr_group":[144633,144903,283244,371909,372368],"msr_project":[638724],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":638724,"post_title":"AI Fairness and Disability","post_name":"ai-fairness-and-disability","post_type":"msr-project","post_date":"2020-02-22 16:11:34","post_modified":"2021-01-20 17:37:14","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-fairness-and-disability\/","post_excerpt":"This project examines issues around AI FATE (Fairness, Accountability, Transparency, and Ethics) and Responsible AI, with a particular focus on how AI systems impact people with disabilities. 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