Bursting the Burden Bubble

An assessement of Sharma et al.'s Counterfactual-Based Fairness Metric.

By Yochem van Rosmalen, Florian van der Steen, Sebastiaan Jans, and Daan van der Weijden.

As presented at the BNAIC/BeNeLearn 2022 conference in Mechelen, Belgium.

Abstract

Machine learning has seen an increase in negative publicity in recent years, due to biased, unfair, and uninterpretable models. There is a rising interest in making machine learning models more fair for unprivileged communities, such as women or people of color. Metrics are needed to evaluate the fairness of a model. A novel metric for evaluating fairness between groups is Burden, which uses counterfactuals to approximate the average distance of negatively classified individuals in a group to the decision boundary of the model. The goal of this study is to compare Burden to statistical parity, a well-known fairness metric, and discover Burden's advantages and disadvantages. We do this by calculating the Burden and statistical parity of a sensitive attribute in three datasets: two synthetic datasets are created to display differences between the two metrics, and one real-world dataset is used. We show that Burden can be more nuanced than statistical parity, but also that the metrics can disagree on which group is treated unfairly. We therefore conclude that Burden is a valuable metric to add to the existing group of fairness metrics, but should not be used on its own.

Read the full paper at bursting-burden.pdf!

Credits

The implementation of CERTIFAI is written by @Ighina, and can be found at github.com/Ighina/CERTIFAI. The Python file of the project is included in this repository: CERTIFAI.py. Licensed MIT.