{"id":685578,"date":"2020-08-19T11:38:41","date_gmt":"2020-08-19T18:38:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=685578"},"modified":"2021-10-20T11:37:00","modified_gmt":"2021-10-20T18:37:00","slug":"lessons-learned-in-designing-ai-for-autistic-adults","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/lessons-learned-in-designing-ai-for-autistic-adults\/","title":{"rendered":"Lessons Learned in Designing AI for Autistic Adults"},"content":{"rendered":"
Through an iterative design process using Wizard of Oz (WOz) prototypes, we designed a video calling application for people with Autism Spectrum Disorder. Our Video Calling for Autism prototype provided an Expressiveness Mirror that gave feedback to autistic people on how their facial expressions might be interpreted by their neurotypical conversation partners. This feedback was in the form of emojis representing six emotions and a bar indicating the amount of overall expressiveness demonstrated by the user. However, when we built a working prototype and conducted a user study with autistic participants, their negative feedback caused us to reconsider how our design process led to a prototype that they did not find useful. We reflect on the design challenges around developing AI technology for an autistic user population, how Wizard of Oz prototypes can be overly optimistic in representing AI-driven prototypes, how autistic research participants can respond differently to user experience prototypes of varying fidelity, and how designing for people with diverse abilities needs to include that population in the development process.<\/p>\n","protected":false},"excerpt":{"rendered":"
Through an iterative design process using Wizard of Oz (WOz) prototypes, we designed a video calling application for people with Autism Spectrum Disorder. Our Video Calling for Autism prototype provided an Expressiveness Mirror that gave feedback to autistic people on how their facial expressions might be interpreted by their neurotypical conversation partners. This feedback was […]<\/p>\n","protected":false},"featured_media":685584,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"ACM","msr_pages_string":"","msr_page_range_start":"1","msr_page_range_end":"6","msr_series":"","msr_volume":"","msr_copyright":"ACM, New York, NY, USA, 6 pages. https: \/\/doi.org\/10.1145\/3373625.3418305","msr_conference_name":"ASSETS 2020: The 22nd International ACM SIGACCESS Conference on Computers and 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