SRI Seminar Series: Catherine Stinson (in person)

Our weekly SRI Seminar Series welcomes Catherine Stinson, Queen’s National Scholar in the Philosophical Implications of AI and assistant professor in computing and philosophy at Queen’s University. With a background in machine learning and philosophy of science, Stinson leads the Ethics & Technology Lab, which explores methodological analyses of AI research, sociological studies, and empirical work in ethical AI, data justice initiatives, and artistic collaborations. Their interdisciplinary work provides critical insights into the ethical and epistemic challenges facing AI research today.

In this talk, Stinson will examine the rise of benchmark datasets in AI, such as ImageNet, and their role in advancing deep learning, critiquing the overly enthusiastic embrace of benchmarks as a harmful practice that can distort research incentives. Drawing on the literature on scientific progress, Stinson contends that critique should be embraced as an essential component of research and that broader inclusion of diverse expertise is essential for progress in AI development.

This seminar is co-hosted by the University of Toronto’s Institute for the History and Philosophy of Science and Technology, and will be presented in-person with an online option at the University of Toronto’s Rotman School of Management.

Moderator: Karina Vold

Venue:

Rotman School of Management, University of Toronto, Room 1065.

Entrance: 95 St. George Street, Toronto, ON M5S 3E6

Title: “Artificial intelligence benchmarks and degenerating research”

Abstract:

The move toward creating massive benchmark datasets, starting with ImageNet, ushered in the rise to prominence of deep learning, and along with it the proliferation of benchmarks for evaluating AI models at performing tasks like recognizing faces, translating languages, and captioning images. Although benchmarks have undeniably been key tools in the rise of deep learning, some researchers have critiqued an overly enthusiastic embrace of benchmarks (a.k.a. benchmark chasing) as a harmful practice, both ethically and epistemically.

Benchmarks can reify narrow views of correct task performance, distort research incentives, and favor researchers at wealthy institutions that can most afford to train state-of-the-art models. Recent empirical evidence has begun to track and quantify the effects and magnitude of the bias toward industry-sponsored versus academic research in areas like natural language processing, raising concerns about corporate capture of research.

Criticism of benchmark chasing is often met with pushback. Defenders of AI insist that critics are trying to move the goalposts each time models manage to surpass a benchmark, and the debate has sometimes devolved to name calling. Drawing on the literature on scientific progress, I argue that critique should be embraced as an essential component in a progressive research program, rather than silenced, and that the boundaries around who is considered a relevant expert worth heeding are too narrow.

About the speaker

Catherine Stinson is Queen’s National Scholar in the Philosophical Implications of Artificial Intelligence and assistant professor in computing and philosophy at Queen’s University. Stinson’s previous positions include postdocs at the Max Planck Institute for Biological Cybernetics and the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, and senior policy associate at the Mowat Centre, University of Toronto. With graduate training in both machine learning and philosophy of science, plus experience in policy and visual art, Stinson brings multiple perspectives to the study of AI. The research in their Ethics & Technology Lab includes methodological analyses of AI research, data-driven sociological studies of AI as a field, empirical work in ethical AI, technical support for data justice initiatives, and collaborations with artists.

About the SRI Seminar Series

The SRI Seminar Series brings together the Schwartz Reisman community and beyond for a robust exchange of ideas that advance scholarship at the intersection of technology and society. Seminars are led by a leading or emerging scholar and feature extensive discussion.

To register for all seminar events in the Winter 2025 season, please contact us directly at sri.research@utoronto.ca.

About the Schwartz Reisman Institute for Technology and Society

The Schwartz Reisman Institute for Technology and Society is a research institute at the University of Toronto that explores the ethical and societal implications of technology. Our mission is to deepen knowledge of technologies, societies, and humanity by integrating research across traditional boundaries to build human-centred solutions.

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