Two Simple Ways to Learn Individual Fairness Metrics from Data

ICML

Cite Paper

Authors

Debarghya Mukherjee
Mikhail Yurochkin
Moulinath Banerjee
Yuekai Sun

Published on

06/19/2022

Categories

ICML Machine Learning

Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that encodes our intuition of what is fair and unfair for the ML task at hand, and the lack of a widely accepted fair metric for many ML tasks is the main barrier to broader adoption of individual fairness. In this paper, we present two simple ways to learn fair metrics from a variety of data types. We show empirically that fair training with the learned metrics leads to improved fairness on three machine learning tasks susceptible to gender and racial biases. We also provide theoretical guarantees on the statistical performance of both approaches.

This paper has been published at ICML 2020

Please cite our work using the BibTeX below.

@misc{mukherjee2020simple,
      title={Two Simple Ways to Learn Individual Fairness Metrics from Data}, 
      author={Debarghya Mukherjee and Mikhail Yurochkin and Moulinath Banerjee and Yuekai Sun},
      year={2020},
      eprint={2006.11439},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}