Guest Lecture: Sébastien Gambs "Synergies and tensions between privacy and other ethical issues in responsible machine learning"

Synergies and tensions between privacy and other ethical issues in responsible machine learning

Abstract : The success of machine learning models is such that they are now ubiquitous in our society. Their widespread use also raises serious privacy and ethical issues, however, especially if their predictions are put into action in domains in which they can significantly affect individuals. As a result, we have witnessed in recent years several initiatives proposing design principles and guidelines for the responsible development of artificial intelligence. To understand how we may best address privacy and ethics responsibly when developing machine learning models, we therefore need to first have a clear view on how these concepts interact with each other in a positive as well as negative manner. In this talk, I will review the main tensions but also convergences that can emerge when addressing jointly the privacy and ethical challenges that go into designing and deploying machine learning models.

Short biography: 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.