Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 80(2018)
摘要
We introduce a new family of fairness definitions that interpolate between statistical and individual notions of fairness, obtaining some of the best properties of each. We show that checking whether these notions are satisfied is computationally hard in the worst case, but give practical oracle-efficient algorithms for learning subject to these constraints, and confirm our findings with experiments.
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