Learning Representational Disparities
This work addresses fairness in machine learning for domains like credit scoring and healthcare, but it is incremental as it builds on prior fair representation methods by incorporating outcome considerations.
The paper tackles the problem of reducing outcome disparities in human decision-making by modeling interpretable representational differences between observed and desired decisions, and proves that their neural network approach can fully mitigate these disparities under certain assumptions.
We propose a fair machine learning algorithm to model interpretable differences between observed and desired human decision-making, with the latter aimed at reducing disparity in a downstream outcome impacted by the human decision. Prior work learns fair representations without considering the outcome in the decision-making process. We model the outcome disparities as arising due to the different representations of the input seen by the observed and desired decision-maker, which we term representational disparities. Our goal is to learn interpretable representational disparities which could potentially be corrected by specific nudges to the human decision, mitigating disparities in the downstream outcome; we frame this as a multi-objective optimization problem using a neural network. Under reasonable simplifying assumptions, we prove that our neural network model of the representational disparity learns interpretable weights that fully mitigate the outcome disparity. We validate objectives and interpret results using real-world German Credit, Adult, and Heritage Health datasets.