Sort Harms by Allocation, Quality, and Representation
Classify common AI fairness issues by the kind of harm they create so the audit focus becomes clearer.
Sort each issue by the kind of harm it mainly represents. Allocation harm Quality-of-service harm Representation harm Qualified applicants from one group are advanced to interviews much less often Speech recognition fails more often for one accent group, leading to slower service Image search consistently underrepresents older women in leadership results Students from one district are offered fewer tutoring slots by the ranking system A translation feature produces poorer support replies for users in one language group A generated avatar set defaults executives to young men unless prompted otherwise
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