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Revisiting Explainable AI through Model-Independent Concept Dictionaries
Thomas Schnake, Doreen Schöppenthau, Alexander Meyer +3
Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transparency. However, existing XAI…
Investigating the Robustness of Subtask Distillation under Spurious Correlation
Pattarawat Chormai, Klaus-Robert Müller, Grégoire Montavon
Subtask distillation is an emerging paradigm in which compact, specialized models are extracted from large, general-purpose 'foundation models' for deployment in environments with…
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
Florian Bley, Jacob Kauffmann, Simon León Krug +2
Distance-based classifiers, such as k-nearest neighbors and support vector machines, continue to be a workhorse of machine learning, widely used in science and industry. In practic…
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Julius Hense, Mina Jamshidi Idaji, Oliver Eberle +7
Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in…
XpertAI: uncovering regression model strategies for sub-manifolds
Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon
In recent years, Explainable AI (XAI) methods have facilitated profound validation and knowledge extraction from ML models. While extensively studied for classification, few XAI so…
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke +5
Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a g…
An XAI framework for robust and transparent data-driven wind turbine power curve models
Simon Letzgus, Klaus-Robert Müller
Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent…
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
Lorenz Linhardt, Klaus-Robert Müller, Grégoire Montavon
Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain…
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller +1
Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of…
DORA: Exploring Outlier Representations in Deep Neural Networks
Kirill Bykov, Mayukh Deb, Dennis Grinwald +2
Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal representations. However, the concepts they learn remain opaque, a problem that becomes par…
XAI for Transformers: Better Explanations through Conservative Propagation
Ameen Ali, Thomas Schnake, Oliver Eberle +3
Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transpar…
Toward Explainable AI for Regression Models
Simon Letzgus, Patrick Wagner, Jonas Lederer +3
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear…
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Christopher J. Anders, David Neumann, Wojciech Samek +2
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence…
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder +2
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processi…