Responsible continual learning

Abstract - Lifelong learning from non-stationary data streams remains a long-standing challenge for machine learning as incremental learning might lead to catastrophic forgetting or interference. Existing works mainly focus on how to retain valid knowledge learned thus far without hindering learning new knowledge and refining existing knowledge when necessary. Despite the strong interest on responsible AI, including aspects like fairness, explainability etc, such aspects are not yet addressed in the context of continual learning.

Biographie - Eirini Ntoutsi is a professor for Artificial Intelligence at the Free University (FU) Berlin. Prior to that, she was an associate professor of Intelligent Systems at the Leibniz University of Hanover (LUH), Germany. Before that, she was a post-doctoral researcher at the Ludwig-Maximilians-University (LMU) in Munich. She holds a Ph.D. from the University of Piraeus, Greece, and a master's and diploma in Computer Engineering and Informatics from the University of Patras, Greece. Her research lies in the fields of Artificial Intelligence (AI) and Machine Learning (ML) and aims at designing intelligent algorithms that learn from data continuously following the cumulative nature of human learning while mitigating the risks of the technology and ensuring long-term positive social impact. However responsibility aspects are even more important in such a setting. In this talk, I will cover some of these aspects, namely fairness w.r.t. some protected attribute(s), explainability of model decisions and unlearning due to e.g., malicious instances.

https://www.mi.fu-berlin.de/en/inf/groups/ag-KIML/members/Professoren/Ntoutsi.html