Grafstein Lecture in Communications

The annual Grafstein Lecture in Communications was established by Senator Jerry S. Grafstein, Q.C., Class of 1958, to commemorate the 40th anniversary of his graduation from the Faculty of Law and the 10th anniversary of the graduation of his son, Laurence Grafstein and daughter-in-law, Rebecca Grafstein (nee Weatherhead), both from the Class of 1988.

Preceding this talk, please join the Hon. Jerry S. Grafstein, K.C. (JD 1958), for light refreshments in the Rowell Room as he shares his latest non-fiction book, The Fractured Twentieth Century (Mosaic Press 2022). Copies will be made available for purchase at this event. Please register for the reception here.

Grafstein Lecture in Communications: "Ground Truth Trouble: Investigating the Foundations of A"

Speaker: Kate Crawford, Research Professor, USC Annenberg School for Communication and Journalism and author of, Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence (Yale U Press 2021)

Ground Truth Trouble: Investigating the Foundations of A"

Abstract: The last decade has seen a dramatic capture of digital material for machine learning production. This data is the basis for sense-making in AI, not as classical representations of the world with individual meaning, but as mass collections: ground truth for machine abstractions and operations. OpenAI’s GPT-3 language model is trained on a corpus of 1 billion words, ImageNet contains over 14 million images, and Tencent’s ML Images contains more than 17.5 million annotated images – predominantly scraped from the internet. Training datasets shape the epistemic boundaries governing how machine learning operates, and thus are an essential part of understanding socially significant questions about AI. But when we closely investigate the benchmark training sets widely used in NLP and computer vision systems, we find complex social, political, and epistemological challenges. What happens when data is seen as an aggregate, stripped of context, meaning, and specificity? In what ways does training data limit what and how machine learning systems interpret the world? And most importantly, what forms of power do these approaches enhance and enable? In this lecture, Kate Crawford will share new work that reflects on what’s at stake in the architecture and contents of training sets, and how they are increasingly part of our urban, legal, logistical, and commercial infrastructures.

Professor Kate Crawford is a leading international scholar of the social implications of artificial intelligence. She is a Research Professor at USC Annenberg in Los Angeles, a Senior Principal Researcher at MSR in New York, an Honorary Professor at the University of Sydney, and the inaugural Visiting Chair for AI and Justice at the École Normale Supérieure in Paris. Her latest book, Atlas of AI (Yale, 2021) won the Sally Hacker Prize from the Society for the History of Technology, the ASSI&T Best Information Science Book Award, and was named one of the best books in 2021 by New Scientist and the Financial Times. Over her twenty-year research career, she has also produced groundbreaking creative collaborations and visual investigations. Her project Anatomy of an AI System with Vladan Joler is in the permanent collection of the Museum of Modern Art in New York and the V&A in London, and was awarded with the Design of the Year Award in 2019 and included in the Design of the Decades by the Design Museum of London. Her collaboration with the artist Trevor Paglen, Excavating AI, won the Ayrton Prize from the British Society for the History of Science. She has advised policy makers in the United Nations, the White House, and the European Parliament, and she currently leads the Knowing Machines Project, an international research collaboration that investigates the foundations of machine learning.

Location: Jackman Law Building, 78 Queens Park, Room: Rosalie Silberman Abella Moot Court Room(J250)

ZOOM guests: The zoom link will be provided through Eventbrite to your email on February 8, 2023 at 5:00pm.