DetoxAI: A Python Toolkit for Debiasing Deep Learning Models in Computer Vision
Abstract
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification tasks, which rely heavily on deep learning. To bridge this gap, we introduce DetoxAI, an open-source Python library for improving fairness in deep learning vision classifiers through post-hoc debiasing. DetoxAI implements state-of-the-art debiasing algorithms, fairness metrics, and visualization tools. It supports debiasing via interventions in internal representations and includes attribution-based visualization tools and quantitative algorithmic fairness metrics to show how bias is mitigated. This paper presents the motivation, design, and use cases of DetoxAI, demonstrating its tangible value to engineers and researchers.
Cite
Text
Stepka et al. "DetoxAI: A Python Toolkit for Debiasing Deep Learning Models in Computer Vision." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_39Markdown
[Stepka et al. "DetoxAI: A Python Toolkit for Debiasing Deep Learning Models in Computer Vision." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/stepka2025ecmlpkdd-detoxai/) doi:10.1007/978-3-032-06129-4_39BibTeX
@inproceedings{stepka2025ecmlpkdd-detoxai,
title = {{DetoxAI: A Python Toolkit for Debiasing Deep Learning Models in Computer Vision}},
author = {Stepka, Ignacy and Sztukiewicz, Lukasz and Wilinski, Michal and Stefanowski, Jerzy},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2025},
pages = {502-505},
doi = {10.1007/978-3-032-06129-4_39},
url = {https://mlanthology.org/ecmlpkdd/2025/stepka2025ecmlpkdd-detoxai/}
}