FairST: A novel approach for machine learning bias repair through latent sensitive attribute translation

Abstract

Context:

As Machine Learning (ML) models are increasingly used in critical decision-making software, concerns have been raised about these systems perpetuating or exacerbating existing historical biases. Consequently, there has been a growing research interest in developing methods to test for fairness and repair biases in ML software, particularly for legally protected attributes like gender, age, race.

Objectives:

In this work, we set out to repair bias for both single and multiple protected attributes (a.k.a. intersectional fairness) of pre-trained machine learning models.

Methods:

We propose a novel model- and task-agnostic debiasing method, Fair Subgroup Translation (FairST), based on fair representation learning via auto-encoders. To the best of our knowledge, this is the first approach based on the principle of Fair Representation Learning devised for post-processing bias repair.

Results:

We empirically evaluate the effectiveness of using FairST to repair a pre-trained Neural Network model used for seven classification tasks involving both single and multiple protected attributes, and benchmark its performance with state-of-the-art fairness repair methods (i.e., Learning Fair Representations, Reweighing, FairBalance and FairMask). We also investigate if the effectiveness of FairST varies when using it to repair bias of other popular ML models (namely Logistic Regression, Support Vector Machine, Gaussian Naive Bayes, Decision Tree and Random Forest).

Conclusion:

The results demonstrate that FairST consistently achieves superior single and intersectional fairness with respect to all benchmarking methods for all classification tasks considered in our empirical study. This supports the potential of using FairST for ML bias repair, and opens up a rich agenda of future work including its application to repair bias arising in tasks of a different nature such as multi-class or image-based problems.