Reducing Unintended Bias of ML Models on Tabular and Textual Data

Guilherme Alves (1) , Maxime Amblard (2) , Fabien Bernier (1) , Miguel Couceiro (1) , Amedeo Napoli (1)

1 ORPAILLEUR - Knowledge representation, reasonning
2 SEMAGRAMME - Semantic Analysis of Natural Language

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.

Mots clés: Bias in machine learning; fair classification model; feature importance; feature dropout; ensemble classifier; post-hoc explanations

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Guilherme Alves, Maxime Amblard, Fabien Bernier, Miguel Couceiro, Amedeo Napoli. Reducing Unintended Bias of ML Models on Tabular and Textual Data. DSAA 2021 - 8th IEEE International Conference on Data Science and Advanced Analytics, Oct 2021, Porto (virtual event), Portugal. pp.1-10. ⟨hal-03312797v2⟩