Conférencier.ère.s invité.e.s

Sébastien Gambs

Biographie

Sébastien Gambs has held the Canada Research Chair in Privacy and Ethical Analysis of Massive Data since December 2017 and has been a professor in the Department of Computer Science at the Université du Québec à Montréal since January 2016. His main research theme is privacy in the digital world. He is also interested in solving long-term scientific questions such as the existing tensions between massive data analysis and privacy as well as ethical issues such as fairness, transparency and algorithmic accountability raised by personalized systems.

Understanding and Addressing Fairwashing in Machine Learning

Fairwashing refers to the risk that an unfair black-box model can be explained by a fairer model through post-hoc explanation manipulation. In this talk, I will first discuss how fairwashing attacks can transfer across black-box models, meaning that other black-box models can perform fairwashing without explicitly using their predictions. This generalization and transferability of fairwashing attacks imply that their detection will be difficult in practice. Finally, I will nonetheless review some possible avenues of research on how to limit the potential for fairwashing.

Catuscia Palamidessi

Biographie

Catuscia Palamidessi is Director of Research at Inria Saclay (since 2002), where she leads the team COMETE. She has been a Full Professor at the University of Genova, Italy (1994-1997) and at Penn State University, USA (1998-2002). Her past achievements include the proof of expressiveness gaps between various concurrent calculi, and the development of a probabilistic version of the asynchronous pi-calculus. More recently, she has contributed to establishing the foundations of probabilistic secure information flow, she has proposed an extension of differential privacy, and geo-indistinguishability, an approach to location privacy. In 2019 she has obtained an ERC advanced grant to conduct research on Privacy and Machine Learning. In 2022, she received the Grand Prix of the French Academy of Science. Palamidessi's research interests include Privacy, Machine Learning, Fairness, Secure Information Flow, Formal Methods, and Concurrency.

Information Structures for Privacy and Fairness

The increasingly pervasive use of big data and machine learning is raising various ethical issues, in particular privacy and fairness. In this talk, I will discuss some frameworks to understand and mitigate the issues, focusing on iterative methods coming from information theory and statistics. In the area of privacy protection, differential privacy (DP) and its variants are the most successful approaches to date. One of the fundamental issues of DP is how to reconcile the loss of information that it implies with the need to preserve the utility of the data. In this regard, a useful tool to recover utility is the Iterative Bayesian Update (IBU), an instance of the famous Expectation-Maximization method from Statistics. I will show that the IBU, combined with the metric version of DP, outperforms the state-of-the-art, which is based on algebraic methods combined with the Randomized Response mechanism, widely adopted by the Big Tech industry (Google, Apple, Amazon, ...). Furthermore, I will discuss a surprising duality between the IBU and one of the methods used to enhance metric DP, that is the Blahut-Arimoto algorithm from Rate-Distortion Theory. Finally, I will discuss the issue of biased decisions in machine learning, and will show that the IBU can be applied also in this domain to ensure a fairer treatment of disadvantaged groups.