An Empirical Study of Rich Subgroup Fairness for Machine Learning

24 Aug 2018Michael KearnsSeth NeelAaron RothZhiwei Steven Wu

Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension... (read more)

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