Differentially Private Post-Processing for Fair Regression

1 code implementation • 7 May 2024 • Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao

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.

Density Estimation Fairness +1