Fairness Sample Complexity and the Case for Human Intervention

24 Oct 2019Ananth BalashankarAlyssa Lees

With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accuracy, when subdivided by specific sensitive variable subgroups... (read more)

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