Fair Bayes-Optimal Classifiers Under Predictive Parity

Understand

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.