Machine learning systems now play a much bigger role in many of our social institutions, from education to healthcare to criminal justice. But many scholars have shown the way these systems are built on data that result in the reproduction of structural bias and discrimination. In this talk, Professor Crawford opens the substrates of training data to uncover the historical origins, labor practices, infrastructures, and epistemological assumptions that go into the production of artificial intelligence. Rather than a focus on technically correcting biases, she argues for a recentering of justice and the enforcement of limits on centralized power.

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Kate Crawford, Co-Founder of the AI Now Institute, is a Distinguished Research Professor at NYU and a Principal Researcher at Microsoft Research, and she is a leading scholar of the social implications of data systems, machine learning, and artificial intelligence. She is widely published in academic journals such as Nature, New Media & Society, and Information, Communication & Society, and she has written for The New York Times, Harper’s Magazine, and The Wall Street Journal. She is also a Visiting Professor at the MIT Center for Civic Media, and an Honorary Professor at the University of New South Wales. 

The AI Now Institute at New York University is an interdisciplinary research centre dedicated to understanding the social implications of artificial intelligence. Their work focuses on four core domains: Rights & Liberties; Labour & Automation; Bias & Inclusion; and Safety & Critical Infrastructure.

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This event is a joint production of the Humanising Machine Intelligence Grand Challenge, the 3A Institute, and the Coral Bell School of Asia Pacific Affairs, all at the Australian National University.