Safe and Responsible AI Development Via Climate Applications

Par

Tegan Rajkumar-Maharaj

Schwartz-Reisman Institute for Technology and Society, U. of Toronto

vendredi 23 février 2024, 10:30-11:30 EST, Salle 6214

Pavillon André-Aisenstadt, Université de Montréal, 2920 Chemin de la Tour

Abstract: Machine learning (ML)-based artificial intelligence (AI) systems are increasingly deployed in real-world settings, but we lack a rigorous science to understand or predict their behavior in these settings.  Even when we can formalize the problem we’re addressing in quite clear statistical terms (e.g. supervised learning on a fixed dataset), there is much we still do not understand about how and why deep nets are able to generalize as well as they do, why they fail when they do, how they will perform on out-of-distribution data. This complicates the already-difficult problem of designing safe and responsible methods and norms of AI development -- where do we even begin?  My answer to that question is to begin by helping to address our ongoing climate crisis. There is no one magic solution to all the problems of climate change, but there are ways almost every form of knowledge and expertise (including ML) can help. And developing safe and responsible AI via important real-world problems ensures that our methodology is grounded and practically useful by design. In this talk I give an overview of my research at the intersection of responsible AI and climate change, with a focus on recent work in deep risk mapping.

Bio: Tegan is an Assistant Professor in the Faculty of Information, and an affiliate of the Vector Institute and Schwartz-Reisman Institute for Technology and Society. She is also a managing editor at the Journal of Machine Learning Research (JMLR), the top scholarly journal in machine learning, and co-founder of Climate Change AI (CCAI), an organization which catalyzes impactful work applying machine learning to problems of climate change.  Prior to joining the iSchool, Tegan did her PhD at Mila and Polytechnique Montreal, where she was an NSERC and IVADO awarded scholar with Christopher Pal. Tegan is broadly interested in studying “what goes into” AI systems – not only data, but the broader learning environment including task design and specification, loss function, and regularization; as well as the broader societal context of deployment including privacy considerations, trends and incentives, norms, and human biases. She is concerned and passionate about AI ethics, safety, and the application of ML to environmental management, health, and social welfare.