Optimized Pre-Processing for Discrimination Prevention
Abstract
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 discrimination, limiting distortion in individual data samples, and preserving utility. We characterize the impact of limited sample size in accomplishing this objective. 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.
Cite
Text
Calmon et al. "Optimized Pre-Processing for Discrimination Prevention." Neural Information Processing Systems, 2017.Markdown
[Calmon et al. "Optimized Pre-Processing for Discrimination Prevention." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/calmon2017neurips-optimized/)BibTeX
@inproceedings{calmon2017neurips-optimized,
title = {{Optimized Pre-Processing for Discrimination Prevention}},
author = {Calmon, Flavio and Wei, Dennis and Vinzamuri, Bhanukiran and Ramamurthy, Karthikeyan Natesan and Varshney, Kush R},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {3992-4001},
url = {https://mlanthology.org/neurips/2017/calmon2017neurips-optimized/}
}