Jenny Yijian Huang, Yunyi Shen, et al.
ICML 2025
Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling group discrimination, limiting distortion in individual data samples, and preserving utility. Several theoretical properties are established, including conditions for convexity, a characterization of the impact of limited sample size on discrimination and utility guarantees, and a connection between discrimination and estimation. Two instances of the proposed optimization are applied to datasets, including one on real-world criminal recidivism. Results show that discrimination can be greatly reduced at a small cost in classification accuracy and with precise control of individual distortion.
Jenny Yijian Huang, Yunyi Shen, et al.
ICML 2025
Flavio Du Pin Calmon, Dennis Wei, et al.
NeurIPS 2017
Dennis Wei, Inkit Padhi, et al.
NeurIPS 2024
Dennis Wei, Rahul Nair, et al.
NeurIPS 2022