Reducing Unintended Bias of ML Models on Tabular and Textual Data
This work addresses fairness concerns in ML models for public trust, but it is incremental as it builds on an existing framework with specific improvements.
The paper tackled the problem of reducing unintended biases in machine learning models by improving the FixOut framework to automate parameter selection and extend its applicability from tabular to textual data, showing that it enhances process fairness without compromising performance in various classification settings.
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.