When Algorithms Make Mistakes: Who Pays the Price?
An AI system designed to predict recidivism rates recommends harsher sentences for certain demographic groups. A hiring algorithm screens out qualified female candidates. A medical diagnostic system misses rare conditions in patients of color. In each case, the consequences are real and significant. But who bears responsibility for these failures?
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