The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …

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

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction,
without achieving true language understanding. As a result, these models fail to generalize …

Various attribution methods have been developed to explain deep neural networks (DNNs)
by inferring the attribution/importance/contribution score of each input variable to the final …

Over the past decade, there has been a growing surge of interest in leveraging artificial
intelligence and machine learning models to address real‐world challenges within the field …

Various attribution methods have been developed to explain deep neural networks (DNNs)
by inferring the attribution/importance/contribution score of each input variable to the final …

Language models (LMs), despite their advances, often depend on spurious correlations,
undermining their accuracy and generalizability. This study addresses the overlooked …

This paper introduces the system of game-theoretic interactions, which connects both the
explanation of knowledge encoded in a deep neural networks (DNN) and the explanation of …

PredDiff is a model-agnostic, local attribution method that is firmly rooted in probability
theory. Its simple intuition is to measure prediction changes while marginalizing features. In …

We address the task of probabilistic anomaly attribution in the black-box regression setting,
where the goal is to compute the probability distribution of the attribution score of each input …