1 citations · 1 across the 2 of their papers we have counts for
5 papers
cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
Jörg Martin, Stefan Haufe
Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitatin…
Feature salience - not task-informativeness - drives machine learning model explanations
Benedict Clark, Marta Oliveira, Rick Wilming +1
Explainable AI (XAI) promises to provide insight into machine learning models' decision processes, where one goal is to identify failures such as shortcut learning. This promise re…
The effect of whitening on explanation performance
Benedict Clark, Stoyan Karastoyanov, Rick Wilming +1
Explainable Artificial Intelligence (XAI) aims to provide transparent insights into machine learning models, yet the reliability of many feature attribution methods remains a criti…
Explainable AI Methods for Neuroimaging: Systematic Failures of Common Tools, the Need for Domain-Specific Validation, and a Proposal for Safe Application
Nys Tjade Siegel, James H. Cole, Mohamad Habes +3
Trustworthy interpretation of deep learning models is critical for neuroimaging applications, yet commonly used Explainable AI (XAI) methods lack rigorous validation, risking misin…
Explainable AI needs formalization
Stefan Haufe, Rick Wilming, Benedict Clark +4
The field of "explainable artificial intelligence" (XAI) seemingly addresses the desire that decisions of machine learning systems should be human-understandable. However, in its c…