Lenders have for many years employed credit scores to assess individuals and the level of risk that they pose. Today, governments are adopting “social credit” scoring systems that serve a similar function.
Social credit itself refers generally to a new mode of data-driven governance through which data analytics are used to create and operate algorithms that provide a basis for rewards and punishments for targeted behaviors. More specifically, it references the specific project of the Chinese state to create a comprehensive legal and regulatory mechanism grounded in data-driven metrics that they have named “social credit.” The phenomenon is not limited to China, however. In the West as well, data-driven governance systems are transforming the regulatory landscape.
In this presentation, Professor Larry Catá Backer will discuss this new form of data-driven social governance. He will examine the implementation challenges that it faces and will consider the resonances of China’s social credit initiatives in the West. He will explore whether accountability regimes grounded in behavior standards enforced through data-driven analytics may soon change the focus of public law from constitution and rule of law to analytics and algorithm.
Professor Backer's lecture is part of the Data Points: Ideas on Data, Law, and Society speaker series, hosted by the Moritz College of Law Program on Data and Governance.
Lunch will be served for advance registrants.