1k citations · 1.1k across the 12 of their papers we have counts for
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Mechanistic understanding and validation of large AI models with SemanticLens
Maximilian Dreyer, Jim Berend, Tobias Labarta +4
Unlike human-engineered systems such as aeroplanes, where each component's role and dependencies are well understood, the inner workings of AI models remain largely opaque, hinderi…
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…
Explaining machine learning models for age classification in human gait analysis
Djordje Slijepcevic, Fabian Horst, Marvin Simak +7
Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing h…
Explaining automated gender classification of human gait
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer +6
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "blac…
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…
Measurably Stronger Explanation Reliability via Model Canonization
Franz Motzkus, Leander Weber, Sebastian Lapuschkin
While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields ne…