# Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps

arXiv: https://arxiv.org/abs/2503.00234 · HTML: https://mlsanity.com/paper/2503.00234/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2025.

## Abstract (arXiv)

The widespread adoption of machine learning systems has raised critical concerns about fairness and bias, making mitigating harmful biases essential for AI development. In this paper, we investigate the relationship between debiasing and removing artifacts in neural networks for computer vision tasks. First, we introduce a set of novel XAI-based metrics that analyze saliency maps to assess shifts in a model's decision-making process. Then, we demonstrate that successful debiasing methods systematically redirect model focus away from protected attributes. Finally, we show that techniques originally developed for artifact removal can be effectively repurposed for improving fairness. These findings provide evidence for the existence of a bidirectional connection between ensuring fairness and removing artifacts corresponding to protected attributes.