Advanced machine learning models have recently achieved high predictive accuracy for
weather and climate prediction. However, these complex models often lack inherent …

While socioeconomic gradients in regional health inequalities are firmly established, the
synergistic interactions between socioeconomic deprivation and climate vulnerability within …

Feature removal is a central building block for eXplainable AI (XAI), both for occlusion-based
explanations (Shapley values) as well as their evaluation (pixel flipping, PF). However …

Originally rooted in game theory, the Shapley Value (SV) has recently become an important
tool in machine learning research. Perhaps most notably, it is used for feature attribution and …

In our work, we propose the use of Representational Similarity Analysis (RSA) for
explainable AI (XAI) approaches to enhance the reliability of XAI-based decision support …

In this paper, we introduce PASTA (Perceptual Assessment System for explanaTion of
Artificial intelligence), a novel framework for a human-centric evaluation of XAI techniques in …

Spurred by the demand for interpretable models, research on eXplainable AI for language
technologies has experienced significant growth, with feature attribution methods emerging …

The perturbation test remains the go-to evaluation approach for explanation methods in
computer vision. This evaluation method has a major drawback of test-time distribution shift …

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive
efforts have been made to make these systems more interpretable or explain their behaviors …

Deep learning's preponderance across scientific domains has reshaped high-stakes
decision-making, making it essential to follow rigorous operational frameworks that include …