Work

Encoding Matters: Impact of Categorical Variable Encoding on Performance and Bias

Year: 2025

Type: conference-paper

Abstract: Encoding categorical variables impacts model performance and can introduce bias in supervised learning, particularly affecting fairness when some groups are under-represented.We analyze the effects of different encoding methods on synthetic and real datasets to mitigate unintended model reliance on specific variables.We propose CaVaR (Categorical Variable Reliance) to quantify model reliance on variables and an Availability Index to measure CaVaR's sensitivity to partial encoding changes.A high Availability Disparity, measured by the standard deviation of the Availability Index across encodings, highlights potential bias from mixed encodings.The results suggest encoding all categorical variables uniformly, regardless of their ordinal or nominal nature, may reduce bias, with the choice guided by computational and performance considerations.1 To facilitate the reading of this paper, we added a glossary in appendix provided here: https://github.com/danielkopp4/ESANN2025Appendix.However, this paper should be readable without the appendix.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-191

Authors: Daniel Kopp, Benjamin Maudet, Lisheng Sun-Hosoya, Kristin Bennett

Institutions: ChaLearn, Université Paris-Saclay

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funder: Agence Nationale de la Recherche