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

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 revealed 83 instances of 6 AI inclusivity bug types unique to user-facing AI products, their fixes covering 47 bug instances, and a new GenderMag inclusive design method variant, GenderMag-for-AI, that is especially effective at detecting AI inclusivity bugs when the AI's output is not necessarily believed.