13 citations · 15 across the 4 of their papers we have counts for
4 papers
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…
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…
Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification
Marta Oliveira, Rick Wilming, Benedict Clark +4
Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improve…