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arXiv · 2510.19033

"Over-the-Hood" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them

Andrew Anderson·Fatima A. Moussaoui·Jimena Noa Guevara·Md Montaser Hamid·Margaret Burnett
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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.

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Andrew Anderson, Fatima A. Moussaoui, Jimena Noa Guevara, Md Montaser Hamid, Margaret Burnett. 2025-10-21. "Over-the-Hood" AI Inclusivity Bugs and How 3 AI Product Teams Found and Fixed Them. https://arxiv.org/abs/2510.19033

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