8 citations · 10 across the 6 of their papers we have counts for
7 papers · 1 filter
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 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…
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
Rick Wilming, Artur Dox, Hjalmar Schulz +3
Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data co…
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
Benedict Clark, Rick Wilming, Artur Dox +11
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalis…
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
Rick Wilming, Leo Kieslich, Benedict Clark +1
In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpret…