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.
Figures & tables
Figure 1: DetoxAI incorporates multiple tools to mitigate biases in vision models. Biased model with debiasing dataset is passed to our module, where biases can be inspected, measured and finally eliminated.
Figure 2: In the upper image, we visualize saliency maps before (Vanilla) and after bias mitigation using a selection of implemented methods. The image on the bottom is a quantitative evaluation of the debiasing techniques on the predictive performance (F1 Score) - fairness trade-off. Fairness metrics are EqualizedOdds, DemographicParity and AccuracyParity, calculated in a difference between protected groups variation [ 5 ] .