544 citations · 547 across the 4 of their papers we have counts for
6 papers
Towards Visually Explaining Statistical Tests with Applications in Biomedical Imaging
Masoumeh Javanbakhat, Piotr Komorowski, Dilyara Bareeva +3
Deep neural two-sample tests have recently shown strong power for detecting distributional differences between groups, yet their black-box nature limits interpretability and practi…
Concept activation vectors: a unifying view and adversarial attacks
Ekkehard Schnoor, Malik Tiomoko, Jawher Said +2
Concept Activation Vectors (CAVs) are a tool from explainable AI, offering a promising approach for understanding how human-understandable concepts are encoded in a model's latent…
Model Science: getting serious about verification, explanation and control of AI systems
Przemyslaw Biecek, Wojciech Samek
The growing adoption of foundation models calls for a paradigm shift from Data Science to Model Science. Unlike data-centric approaches, Model Science places the trained model at t…
Blockchain and Artificial Intelligence: Synergies and Conflicts
Leon Witt, Armando Teles Fortes, Kentaroh Toyoda +2
Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains. This paper explores synergies and challenges between these…
Position: Explain to Question not to Justify
Przemyslaw Biecek, Wojciech Samek
Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incom…
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Luca Longo, Mario Brcic, Federico Cabitza +16
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In respo…