Brian Y. Lim

Towards Relatable Explainable AI with the Perceptual Process

2022 · CHI Conference on Human Factors in Computing Systems

Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current contrastive explanations are rudimentary comparisons between examples or raw features, …

Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning

2020 · arXiv (Cornell University)

Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic …