# Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias

arXiv: https://arxiv.org/abs/1910.07604 · HTML: https://mlsanity.com/paper/1910.07604/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma](https://mlsanity.com/paper/2303.12806/) (2023)
- [Saliency Cards: A Framework to Characterize and Compare Saliency Methods](https://mlsanity.com/paper/2206.02958/) (2022)
- [Domain-guided data augmentation for deep learning on medical imaging](https://mlsanity.com/paper/2210.04977/) (2022)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [Towards Interpretability in Audio and Visual Affective Machine Learning: A Review](https://mlsanity.com/paper/2306.08933/) (2023)
- [Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification](https://mlsanity.com/paper/2506.23247/) (2025)

## Abstract (arXiv)

In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons. In medical imaging, saliency maps have become the standard tool for determining whether a neural model has learned relevant robust features, rather than artefactual noise. However, saliency maps are limited to local model explanation because they interpret predictions on an image-by-image basis. We propose aggregating saliency globally, using semantic segmentation masks, to provide quantitative measures of model bias across a dataset. To evaluate global saliency methods, we propose two metrics for quantifying the validity of saliency explanations. We apply the global saliency method to skin lesion diagnosis to determine the effect of artefacts, such as ink, on model bias.