1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
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…
cs.LG2024★ 1 cited
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…