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- W4280497732 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." @default.
- W4280497732 created "2022-05-22" @default.
- W4280497732 creator A5031235093 @default.
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- W4280497732 date "2022-05-15" @default.
- W4280497732 modified "2023-10-16" @default.
- W4280497732 title "Fair Bayes-Optimal Classifiers Under Predictive Parity" @default.
- W4280497732 doi "https://doi.org/10.48550/arxiv.2205.07182" @default.
- W4280497732 hasPublicationYear "2022" @default.
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