Event

Automating fairness? AI in the Chinese courts

Automating fairness? AI in the Chinese courts

Legal Priorities Project, Friday 10 December 2021

17:00–18:00 GMT; 01:00-02:00 CST; or find your local time here.

How will surging global interest in data analytics and artificial intelligence transform the day-to-day operations of courts, and what are the implications for judicial power? In the last five years, Chinese courts have come to lead the world in their efforts to deploy automated pattern analysis to monitor judges, standardize decision-making, and observe trends in society. This Article chronicles how and why Chinese courts came to embrace artificial intelligence, making public tens of millions of court judgments in the process. Although technology is certainly being used to strengthen social control and boost the legitimacy of the Chinese Communist Party, examining recent developments in the Chinese courts complicates common portrayals of China as a rising exemplar of digital authoritarianism. Data are incomplete, and algorithms are often untested.

Rachel E. Stern is a Professor of Law and Political Science at UC Berkeley and currently holds the Pamela P. Fong and Family Distinguished Chair in China Studies. Her research looks at law in Mainland China and Hong Kong, especially the relationship between legal institution building, political space, and professionalization. Stern is currently part of a collaborative effort to analyze the 60+ million Chinese judicial decisions placed online following a 2014 policy change. This massive expansion in the public record of court activity promises to re-shape our understanding of Chinese law and, beyond China, of authoritarian legality. Before joining Berkeley Law and the Jurisprudence and Social Policy Program, Stern was a Junior Fellow at the Harvard University Society of Fellows. She also currently serves as series editor for the Law and Society series at Cambridge University Press.

Join Zoom Meeting: https://us06web.zoom.us/j/86127303416 | Meeting ID: 861 2730 3416

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