# Bayesian fairness

## TL;DR

This paper introduces Bayesian fairness, a new approach to fair decision-making that explicitly accounts for uncertainty in probabilistic models, improving fairness and performance.

## Contribution

It proposes Bayesian fairness as a novel framework that incorporates parameter uncertainty into fairness criteria, extending existing notions like balance.

## Key findings

- Bayesian fairness can produce well-performing, fair decision rules under high uncertainty.
- Incorporating parameter uncertainty improves fairness in decision-making.
- The approach extends the balance fairness criterion to a Bayesian setting.

## Abstract

We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of {\em Bayesian fairness} as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced by Kleinberg et al (2016), we show how a Bayesian perspective can lead to well-performing, fair decision rules even under high uncertainty.