{"ID":2852435,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19033","arxiv_id":"2510.19033","title":"\"Over-the-Hood\" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them","abstract":"While much research has shown the presence of AI's \"under-the-hood\" biases (e.g., algorithmic, training data, etc.), what about \"over-the-hood\" inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has begun to report the existence of such biases -- but what do they look like, how prevalent are they, and how can developers find and fix them? To find out, we conducted a field study with 3 AI product teams, to investigate what kinds of AI inclusivity bugs exist uniquely in user-facing AI products, and whether/how AI product teams might harness an existing (non-AI-oriented) inclusive design method to find and fix them. The teams' work resulted in identifying 6 types of AI inclusivity bugs arising 83 times, fixes covering 47 of these bug instances, and a new variation of the GenderMag inclusive design method, GenderMag-for-AI, that is especially effective at detecting certain kinds of AI inclusivity bugs.","short_abstract":"While much research has shown the presence of AI's \"under-the-hood\" biases (e.g., algorithmic, training data, etc.), what about \"over-the-hood\" inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has begun to report the e...","url_abs":"https://arxiv.org/abs/2510.19033","url_pdf":"https://arxiv.org/pdf/2510.19033v1","authors":"[\"Andrew Anderson\",\"Fatima A. Moussaoui\",\"Jimena Noa Guevara\",\"Md Montaser Hamid\",\"Margaret Burnett\"]","published":"2025-10-21T19:24:12Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[]","has_code":false}