Combining demographic attributes with visual context in fairness audits reveals worst-group accuracy gaps up to 26 percentage points that aggregate and demographic-only evaluations miss.
In: IEEE International Conference on Data Mining Workshops (2019) 4, 5
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CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis
Combining demographic attributes with visual context in fairness audits reveals worst-group accuracy gaps up to 26 percentage points that aggregate and demographic-only evaluations miss.