Equalizing Financial Impact in Supervised Learning

arXiv: Learning, Volume abs/1806.09211, 2018.

Cited by: 0|Views14


Notions of fair classification that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairness criteria from Hard...More



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