| Libellé du cours : | Advanced Machine Learning 3 - Fairness in Thrustworthy Machine Learning |
|---|---|
| Département d'enseignement : | EEA / Electronique Electrotechnique Automatique |
| Responsable d'enseignement : | Monsieur PIERRE-ANTOINE THOUVENIN / Monsieur PIERRE CHAINAIS |
| Langue d'enseignement : | |
| Ects potentiels : | 0 |
| Grille des résultats : | |
| Code et libellé (hp) : | MR_DS_S3_AM3 - Advanced machine learning 3 |
Equipe pédagogique
Enseignants : Monsieur PIERRE-ANTOINE THOUVENIN / Monsieur PIERRE CHAINAIS
Intervenants extérieurs (entreprise, recherche, enseignement secondaire) : divers enseignants vacataires
Résumé
Nowadays, machine learning methods find widespread application due to their ability to learn models that per-form outstandingly, sometimes reaching human-level capabilities. However, if these models have the potential to impact human lives, for example in justice, solely evaluating them in terms of accuracy is not sufficient any-more. Other notions then need to be considered to ensure that the models are trustworthy and that they do not have a negative impact on individuals. This course will focus on fairness as a trustworthiness concept that aims to eliminate discrimination in machine learning models. First, we will recognize various sources of unfairness before exploring several of the numerous fairness metrics proposed in literature. Then, focusing on a family of measures, we will study several strategies to mitigate these discriminatory behaviors. Finally, we will examine how privacy, another important aspect of trustworthiness, can affect fairness.
Objectifs pédagogiques
The goal of this course is to provide an introduction to the problem of fair machine learning, a core concept for building more trustworthy systems. After successfully completing this course, a student should: • Be able to identify machine learning problems where fairness issues may arise. • Be able to evaluate the degree of unfairness of the models with various metrics. • Be able to deploy some existing solutions to mitigate the discriminatory behaviors. • Be aware of the potential impact that privacy may have on fairness.
Objectifs de développement durable
Modalités de contrôle de connaissance
Contrôle Continu
Commentaires: Practicals, (min) 0 - 20 (max)
Written exam, (min) 0 - 20 (max)
Passing grade 10/20
Ressources en ligne
• "Fairness and Machine Learning: Limitations and Opportunities", Solon Barocas, Moritz hardt, Arvind Narayanan • "The Algorithmic Foundations of Differential Privacy", Cynthia Dwork, Aaron Roth
Pédagogie
24 hours, 6 lectures, 6 practical sessions.
Séquencement / modalités d'apprentissage
| Nombre d'heures en CM (Cours Magistraux) : | 12 |
|---|---|
| Nombre d'heures en TD (Travaux Dirigés) : | 12 |
| Nombre d'heures en TP (Travaux Pratiques) : | 0 |
| Nombre d'heures en Séminaire : | 0 |
| Nombre d'heures en Demi-séminaire : | 0 |
| Nombre d'heures élèves en TEA (Travail En Autonomie) : | 0 |
| Nombre d'heures élèves en TNE (Travail Non Encadré) : | 0 |
| Nombre d'heures en CB (Contrôle Bloqué) : | 0 |
| Nombre d'heures élèves en PER (Travail PERsonnel) : | 0 |
| Nombre d'heures en Heures Projets : | 0 |
Pré-requis
• Basic notions of probabilities and statistics • Basic notions of linear algebra • Core concepts in machine learning (e.g. supervised learning, empirical risk minimization, gradient descent, …) • Python programming (e.g. jupyter notebook, numpy, pandas, scikit-learn, ...)