Towards Explainable AI

Abstract: AI systems have become the de facto tools to solve complex problems in computer vision. Yet, it has been shown that these systems might not actually be safe to be deployed in the real world, as they too often tend to rely on dataset biases and other statistical shortcuts to achieve high performance. A growing body of research thus focuses on the development of explainability methods to better interpret these systems, to make them more trustworthy. In this talk, I will first give a general overview of the methods commonly used in eXplainable AI, before discussing the challenges that still need to be overcome by the community.

Short bio: Julien Colin is an ELLIS PhD student. He holds a Bachelor’s degree in Physics and Chemistry (2019, University of Lorraine) and a Master’s degree in Cognitive Sciences: Natural and Artificial Cognition (2021, INP Grenoble). Before the start of his PhD, he worked as a research assistant; first at ANITI for 6 months (2021, Toulouse) then at Brown University for 5 months (2022, Providence). His PhD topic is centered around eXplainable AI and Cognitive Science. In his research, he is interested in developing methods to better understand Intelligent systems. His supervisors are Nuria Oliver (ELLIS Alicante) and Thomas Serre (ANITI).

Presenter: Julien Colin

Date: 2023-01-30 11:30 (CET)

Location: Salon de Actos Politecnica IV, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES