Learning Representational Disparities

TL;DR

This paper introduces a fair machine learning approach that models and corrects representational disparities between observed and desired decision-making to reduce outcome disparities, using a neural network framework validated on real datasets.

Contribution

It presents a novel neural network-based method to learn interpretable representational disparities that can be corrected to mitigate outcome disparities in decision-making.

Findings

The neural network model learns interpretable weights that fully mitigate outcome disparity under certain assumptions.

Validation on real-world datasets demonstrates the effectiveness of the approach.

The method provides insights into the sources of disparity and potential interventions.

Abstract

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.