Reducing Unintended Bias of ML Models on Tabular and Textual Data

TL;DR

This paper improves fairness in machine learning models by refining the FixOut framework, making it more automated and applicable to both tabular and textual data, while maintaining model performance.

Contribution

The paper introduces automated parameter selection for FixOut and extends its application from tabular to textual data for fairness enhancement.

Findings

FixOut improves process fairness across various classification tasks.

Automated parameter choice simplifies the fairness enhancement process.

FixOut is feasible for textual data, broadening its applicability.

Abstract

Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of ML models that consists in reducing the dependence of models on sensitive features, without compromising their performance. We revisit the framework FixOut that is inspired in the approach "fairness through unawareness" to build fairer models. We introduce several improvements such as automating the choice of FixOut's parameters. Also, FixOut was originally proposed to improve fairness of ML models on tabular data. We also demonstrate the feasibility of FixOut's workflow for models on textual data. We present several experimental results that illustrate the fact that FixOut improves process fairness on different classification settings.