Chair objectives

The goal of the Chair is to build new methods in machine learning to obtain fair and robust algorithms. The presence of bias and discrimination is well acknowledged in machine learning. Instead of providing decisions which appear as sharp and accurate, algorithms may perpetuate or even exacerbate biases in the training data.

The goal of this project is to develop new types of machine learning algorithms, which, while being able to provide efficient forecasts or predictions, do not reflect any bias in their output and thus achieve what is nowadays called fairness in algorithmic decisions.

The number of research papers in this field has grown exponentially over the last few years and recent contributions to the field have proposed various mathematical definitions and novel algorithms for addressing fairness in a wide range of learning problems. Yet few works have managed to derive algorithms which are supported by strong theoretical guarantees and the quality assessment, which is an important requirement to be able to certify fair behaviour of an algorithm.

Programs: Acceptable, certifiable & collaborative AI

Themes: Explainability, Fair Learning

Chair holder:
Jean-Michel Loubès, PR UT3, IMT

Senior collaborating researchers

Matthieu Serrurier (UT3, IRIT),

Béatrice Laurent (INSA Toulouse, IMT)

Website
https://perso.math.univ-toulouse.fr/loubes/

Know more

Jean Michel Loubès, the mathematician who teaches machines

An Exploreur article 

Read the article in French

Jean-Michel Loubes, les algorithmes pourraient rendre la société plus équitable.

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