CPI Talk - Understanding and Addressing Fairwashing in Machine Learning
CPI would like to extend an invitation to our CPI Talk on Thursday Nov. 7 from 2:30 - 4:00pm in SRADM EC5-1111 Enterprise Theatre , taking place in person.
Speaker: Sébastien Gambs - Canada Research Chair (Tier 2) in Privacy-preserving and Ethical Analysis of Big Data, Professor in Computer Science, Université du Québec à Montréal
CPI Talks are free and open to everyone regardless of affiliation! High school students and non-Waterloo students/staff are also welcome to join.
No prior knowledge will be expected from the audience.
Please register here.
In this CPI Talk, Sébastien Gambs will discuss:
Understanding and Addressing Fairwashing in Machine Learning
Abstract:
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, Sébastien 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, Sébastien will nonetheless review some possible avenues of research on how to limit the potential for fairwashing.
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.