(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers

TL;DR

This paper introduces a Bayesian approach to quantify uncertainty in fairness disparities, enabling preference-based selection of decision-makers with the most reliable fairness assessments in machine learning and human decisions.

Contribution

It proposes a novel Bayesian framework that incorporates uncertainty into fairness metrics and a utility-based method for selecting the most reliably fair decision-maker.

Findings

Quantifies uncertainty in fairness disparities using Bayesian statistics.

Provides a utility-based method for selecting decision-makers based on certainty.

Enhances fairness assessment by considering both disparity and its uncertainty.

Abstract

Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains, including machine learning models and human decision-makers in real-world applications. This involves calculating the disparities between probabilistic outcomes among social groups, such as acceptance rates between male and female applicants. However, traditional fairness metrics do not account for the uncertainty in these processes and lack of comparability when two decision-makers exhibit the same disparity. Using Bayesian statistics, we quantify the uncertainty of the disparity to enhance discrimination assessments. We represent each decision-maker, whether a machine learning model or a human, by its disparity and the corresponding uncertainty in that disparity. We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker according to a utility function that ranks decision-makers based on these preferences. The decision-maker with the highest utility score can be interpreted as the one for whom we are most certain that it is fair.