Differentially Private Post-Processing for Fair Regression
This work addresses fairness and privacy concerns for sensitive data in machine learning, though it is incremental as it builds on existing post-processing and differential privacy methods.
The paper tackles the problem of ensuring both fairness and privacy in regression models by introducing a differentially private post-processing algorithm that remaps regressor outputs to satisfy statistical parity, achieving fairness guarantees with a trade-off between statistical bias and variance based on histogram bin choices.
This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing privacy concerns of machine learning models trained on sensitive data, as well as fairness concerns of their potential to propagate historical biases. Our algorithm can be applied to post-process any given regressor to improve fairness by remapping its outputs. It consists of three steps: first, the output distributions are estimated privately via histogram density estimation and the Laplace mechanism, then their Wasserstein barycenter is computed, and the optimal transports to the barycenter are used for post-processing to satisfy fairness. We analyze the sample complexity of our algorithm and provide fairness guarantee, revealing a trade-off between the statistical bias and variance induced from the choice of the number of bins in the histogram, in which using less bins always favors fairness at the expense of error.