# A Causal Bayesian Networks Viewpoint on Fairness

## TL;DR

This paper presents a causal Bayesian network approach to understanding and measuring unfairness in datasets, emphasizing the importance of causal paths and offering insights into fair model design.

## Contribution

It introduces a graphical causal framework to interpret and evaluate unfairness, providing a new perspective for fairness analysis in complex scenarios.

## Key findings

- Causal paths in Bayesian networks reveal sources of unfairness.
- Fairness evaluation must consider underlying causal structures.
- The approach aids in designing models that mitigate unfairness.

## Abstract

We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out that fairness evaluation on a model requires careful considerations on the patterns of unfairness underlying the training data. We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.