We propose an automated algorithm to stress-test a trained visual model by generating
language-guided counterfactual test images (LANCE). Our method leverages recent …

Shortcut learning is when a model–eg a cardiac disease classifier–exploits correlations
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …

Despite increasing progress in development of methods for generating visual counterfactual
explanations, previous works consider them as an entirely local technique. In this work, we …

This paper addresses the challenge of generating Counterfactual Explanations (CEs),
involving the identification and modification of the fewest necessary features to alter a …

Graph Neural Networks (GNNs) have demonstrated significant potential in recommendation
tasks by effectively capturing intricate connections among users, items, and their associated …

Ensuring model explainability and robustness is essential for reliable deployment of deep
vision systems. Current methods for evaluating robustness rely on collecting and annotating …

Deep learning is dramatically transforming the field of medical imaging and radiology,
enabling the identification of pathologies in medical images, including computed …

Trustworthiness is a major prerequisite for the safe application of opaque deep learning
models in high-stakes domains like medicine. Understanding the decision-making process …

Counterfactual explanations have emerged as a promising method for elucidating the
behavior of opaque black-box models. Recently, several works leveraged pixel-space …

Deep neural networks (DNNs) often encounter significant challenges related to opacity,
inherent biases, and shortcut learning, which undermine their practical reliability. In this …