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Fatima A. Moussaoui

Publications and source records attributed to Fatima A. Moussaoui.

2 recordsLinked to original sources

Apply- Mag: One Tool to Support Many Inclusive Design Methods

Doing inclusive design in HCI practice can be labor-intensive, a costly barrier that some companies and HCI practitioners may be unwilling or unable to overcome. Yet, not doing inclusive design is costly too, in the form of UX barriers that disproportionately disadvantage under-served user populations. To address this problem, we introduce Apply- Mag, an LLM-powered tool to support HCI practitioners' work to design their products inclusively to wide ranges of users. Apply- Mag is general, supporting any inclusive design method that can be expressed as Mags (i.e., using attribute ranges and heuristics). It is also effective: Empirical results with researcher and practitioner teams using various combinations of two Mags on 7 products showed Apply- Mag precision averaging 90-99% and recall averaging 82-89%. Further, its environmental costs were reasonable, costing about the same resources as 2-4 ordinary Google searches.

cs.HC↗

"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 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.

cs.HC↗