Fair Bayes-Optimal Classifiers Under Predictive Parity

• URL: http://arxiv.org/abs/2205.07182v1
• Date: Sun, 15 May 2022 04:58:10 GMT
• Title: Fair Bayes-Optimal Classifiers Under Predictive Parity
• Authors: Xianli Zeng, Edgar Dobriban and Guang Cheng
• Abstract summary: This paper considers predictive parity, which requires equalizing the probability of success given a positive prediction among different protected groups. We propose an algorithm we call FairBayes-DPP, aiming to ensure predictive parity when our condition is satisfied.
• Score: 33.648053823193855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based measures (e.g., demographic parity, equality of opportunity, equalized odds), while sufficiency-based measures such as predictive parity are much less studied. This paper considers predictive parity, which requires equalizing the probability of success given a positive prediction among different protected groups. We prove that, if the overall performances of different groups vary only moderately, all fair Bayes-optimal classifiers under predictive parity are group-wise thresholding rules. Perhaps surprisingly, this may not hold if group performance levels vary widely; in this case we find that predictive parity among protected groups may lead to within-group unfairness. We then propose an algorithm we call FairBayes-DPP, aiming to ensure predictive parity when our condition is satisfied. FairBayes-DPP is an adaptive thresholding algorithm that aims to achieve predictive parity, while also seeking to maximize test accuracy. We provide supporting experiments conducted on synthetic and empirical data.

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