Achieving Equalized Odds by Resampling Sensitive Attributes

Abstract

We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This is achieved by introducing a general discrepancy functional that rigorously quantifies violations of this criterion. This differentiable functional is used as a penalty driving the model parameters towards equalized odds. To rigorously evaluate fitted models, we develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature. Both the model fitting and hypothesis testing leverage a resampled version of the sensitive attribute obeying equalized odds, by construction. We demonstrate the applicability and validity of the proposed framework both in regression and multi-class classification problems, reporting improved performance over state-of-the-art methods. Lastly, we show how to incorporate techniques for equitable uncertainty quantification---unbiased for each group under study---to communicate the results of the data analysis in exact terms.

14 pages, 4 figures

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Equality of Opportunity in Supervised Learning
• A tutorial on conformal prediction
• Fairness in Machine Learning
• Learning to Pivot with Adversarial Networks

Cited by in corpus (4)

• What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective
• Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
• FairBalance: How to Achieve Equalized Odds With Data Pre-processing
• Learning to Increase the Power of Conditional Randomization Tests