On March 7th and 8th, ANITI is organizing a workshop on explainability. This workshop, which will be held in B612, will allow to deepen the development of understanding what is an explainable AI.

As machine learning (ML) algorithms and architectures have become more and more complex, the explainability or interpretability of such algorithms and architectures has become a major scientific challenge. On the one hand meeting this challenge is basic to the scientific method; if one can't interpret or explain the behavior of an ML system, then one doesn't understand it. And if one can't understand the behavior of the system, then we can't offer guarantees about what it does; we can't, for instance, certify that the system is safe for use in situations where livelihoods or even lives may be at stake. Thus, interpretability becomes a requirement for ML systems that we want to use in application areas like transport, medicine, or economic or environmental forecasting.

Program

Monday 7.03

10h 

Introduction 

10.30

Joao Marques-Silva : Logic based explanations

11.30

présentation de David Vigouroux - Formal interpretability

12h 

lunch break

2.00

Leila Amgoud - Axiomatic Foundations of Explainability

3.00

break

3.30

Serena Villata Towards Natural Language Explanatory Argument Generation: Achieved Results and Open Challenges

Tuesday 8.03

9.15

Michèle Sebag

10.30

Lucas de Lara From structural to optimal transport counterfactuals 

11.30

Karima Maklouf, Sami Zhioua, Causality for Fairness and Explainability in ML models

12.30

lunch break

14.00

Thibault Laugel, Understanding prediction discrepancies in ML classifiers

15.00

Philippe Muller : Explaining semantic representations in natural language processing

16.00

End of the workshop

Following the workshop, ANITI organizes an afterwork on "Explainability". Tuesday, 8th march, from 5 to 6.30 pm  in the B612

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