Artificial Intelligence and the Uncertainty challenge in Fundamental Physics

Session

Explainable AI

28 nov. 2023, 09:00

Présidents de session

Explainable AI: Amphi Durand, batiment Esclangon

• Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)

33. xAI in practice: current state of the art, limitations and perspectives

Julien Girard-Satabin (CEA)

28/11/2023 09:00

Explainable Artificial Intelligence (xAI) is a vibrant research field that aims to provide an insight on the decision taken by machine learning (ML) programs. The pervasiveness of AI in our societes pushed regulations (as the European AI Act) that demands transparency. We will present an overview of the field of Explainable AI, focusing on local explanations. We will present the various caveat...

32. Explainable AI for Interpretability of Deep Neural Networks : the High Energy Physics perspective

Mark Neubauer (University of Illinois at Urbana-Champaign)

28/11/2023 09:45

Explainable AI (xAI) represents a set of processes and methods that allows human users to comprehend results created by machine learning algorithms. In the context of applications of AI to science, we need to look beyond standard metrics of prediction performance such as accuracy to ensure that AI models are robust to noise and adversarial samples, fair to biases in data populations, and...

5. Advancing Explainable AI: Testing and Enhancing Techniques Across Multidisciplinary Use-Cases

Simone Scardapane (Sapienza University of Rome, Roma, Italy)

28/11/2023 10:35

Explainable AI, Trustworthy AI

Developing and testing methodologies for enhancing the transparency, interpretability, and explainability of AI algorithms is a pressing challenge for the application of artificial intelligence methods in fundamental physics. The Multi-disciplinary Use Cases for Convergent new Approaches to AI explainability (MUCCA) project is an innovative project that aims to address this challenge by...