On conditional parity as a notion of non-discrimination in machine learning

Ya'acov Ritov, Yuekai Sun, Ruofei Zhao

We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying randomization as a general mechanism for achieving conditional parity and a kernel-based test of conditional parity.

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