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Learning Optimal Fair Policies

This is an implementation of the following paper: Learning Optimal Fair Policies (ICML, 2019)

Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or unfairness in our society. In this paper, we consider how to make optimal but fair decisions, which “break the cycle of injustice” by correcting for the unfair dependence of both decisions and outcomes on sensitive features (e.g., variables that correspond to gender, race, disability, or other protected attributes). We use methods from causal inference and constrained optimization to learn optimal policies in a way that addresses multiple potential biases which afflict data analysis in sensitive contexts, extending the approach of Nabi & Shpitser (2018). Our proposal comes equipped with the theoretical guarantee that the chosen fair policy will induce a joint distribution for new instances that satisfies given fairness constraints. We illustrate our approach with both synthetic data and real criminal justice data.

If you find it useful, please consider citing:

title = {Learning Optimal Fair Policies},
author = { Razieh Nabi and Daniel Malinsky and Ilya Shpitser},
booktitle = {Proceedings of the Thirty Sixth International Conference on Machine Learning (ICML-36th)},
year = { 2019 }, 
publisher = {PMLR}, 
volume = {97}, 
pages = {4674-4682}




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