Portrait de Ulrich Aivodji

Ulrich Aivodji

Membre académique associé
Professeur associé, École de technologie supérieure (ETS), Département de génie logiciel et des TI
École de technologie supérieure
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Exploration des données
Optimisation
Site web
Google Scholar

Biographie

Ulrich Aivodji est professeur associé d'informatique au Département de génie logiciel et des technologies de l'information de l’École de technologie supérieure de Montréal (ÉTS).

Il dirige le Trustworthy Information Systems Lab (TISL). Ses domaines de recherche sont la sécurité informatique, la confidentialité des données, l'optimisation et l'apprentissage automatique. Ses travaux actuels portent sur plusieurs aspects de l'apprentissage automatique digne de confiance, tels que l'équité, l'apprentissage automatique préservant la vie privée et l'explicabilité.

Avant d'occuper son poste actuel, il était chercheur postdoctoral à l'Université du Québec à Montréal (UQAM), où il travaillait avec Sébastien Gambs sur l'éthique de l'apprentissage automatique et la protection de la vie privée. Il a obtenu un doctorat en informatique à l'Université Paul-Sabatier, sous la direction de Marie-José Huguet et Marc-Olivier Killijian. Pendant son doctorat, il a été affilié au Laboratoire de recherche spécialisé dans l’analyse et l’architecture des systèmes du Centre national de la recherche scientifique (LAAS-CNRS) en tant que membre des groupes de recherche Informatique fiable et tolérance aux fautes et Recherche opérationnelle, optimisation combinatoire et contraintes.

Étudiants actuels

Maryam Babaei
Doctorat - École de technologie suprérieure
Github
Eliott Baltz
Maîtrise recherche - École de technologie suprérieure
Github
Zahra Batool
Postdoctorat - École de technologie suprérieure
zahra.batool@mila.quebec
Github
Chayma Ben Abdeljelil
Maîtrise recherche - École de technologie suprérieure
Github
Meghana Bhange
Maîtrise recherche - École de technologie suprérieure
Co-superviseur⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
Patrik Kenfack
Doctorat - École de technologie suprérieure
Co-superviseur⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
Lena Libon
Stagiaire de recherche - École de technologie suprérieure (ÉTS)
Site web
Killijian Marc-Olivier
Visiteur de recherche indépendant - UQAM
Laurent Mathe
Stagiaire de recherche - École de technologie suprérieure (ÉTS)
Hasti Nafisi
Collaborateur·rice de recherche - UdeM
Github
Louis Tremblay Thibault
Doctorat - École de technologie suprérieure
Site web

Publications

Local Data Debiasing for Fairness Based on Generative Adversarial Training
Ulrich Aivodji
François Bidet
Sébastien Gambs
Rosin Claude Ngueveu
Alain Tapp
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness o… (voir plus)f the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.
2021-03-14
Algorithms (publié)
doi.org
arxiv.org
Fairwashing: the risk of rationalization
Ulrich Aivodji
Hiromi Arai
Olivier Fortineau
Sébastien Gambs
Satoshi Hara
Alain Tapp
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and general… (voir plus)ly complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.
2019-05-24
Proceedings of the 36th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org