1k citations · 1.1k across the 7 of their papers we have counts for
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cs.LG2022★ 1 cited
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
Alexander Binder, Leander Weber, Sebastian Lapuschkin +3
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifical…
cs.LG2022★ 10 cited
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder +1
Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of…