Chair objectives

The aim of this project is to use symbolic AI approaches, namely (modal) logic and argumentation theory, for explaining and improving predictions of data-driven models.

We are particularly interested in different types of classifiers (binary, multi-class, and multi-label), and in both black-box models (whose internal reasoning is left unspecified) and white-box models including Naive Bayes, Decision Trees, and Random Forest.

The chair has four main research threads:

  • Theoretical foundations of explainability
  • Explainability of black-box models
  • Explainability of white-box models
  • Natural Language Processing

Programs: Acceptable, certifiable & collaborative AI

Themes: Explicability, Fair Learning

Chair holder:

Leïla Amgoud, DR CNRS, IRI

Senior collaborating researchers

Emiliano LORINI (CNRS, IRIT),

Philippe MULLER (UT3, IRIT)

Website

https://www.irit.fr/~Leila.Amgoud

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More about Leila Amgoud

An Exploreur article, in French 

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