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

2017/06/26 by Ya’acov Ritov, Yuekai Sun, Ritov, Ya'acov +3 · 2 citations
Computer Science · #Adversarial Robustness in Machine Learning #Computers and Society (cs.CY) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1706.08519

openalex publication_date 2017/06/26 · openalex created_date 2017/06/30 · openalex updated_date 2026/07/28

Abstract

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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