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Poster
Fair Bayes-Optimal Classifiers Under Predictive Parity
Xianli Zeng · Edgar Dobriban · Guang Cheng

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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.

Keywords: Group-wise thresholding rule · Predictive parity · Fair-Bayes-DPP algorithm · Fair Bayes-optimal classifier

Author Information

Xianli Zeng (Shenzhen Research Institute of Big Data, University of Pennsylvania)
Edgar Dobriban (University of Pennsylvania)
Guang Cheng (University of California, Los Angeles)

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