Automated Law Enforcement

An assessment of China’s Social Credit Systems (SCS) using interview evidence from Shanghai

Abstract

This paper provides one of the first fieldwork-based research accounts of China's Social Credit Systems (SCS).  It focuses on the issue of automated law enforcement. Evidence is drawn from semi-structured interviews with Shanghai-based local government officials, judges and corporate employees, conducted in April 2021. These are actors who supervise, manage, and/or operate Shanghai’s SCS at the level of daily practice. The paper examines the use of blacklists and joint sanctions within the wider framework of the SCS. The interview evidence, combined with online archival research, uncovers a more complete understanding than previously available of the detailed workings of these systems and of their perceived impacts, both positive and negative, in the field. Automation is observed to have achieved efficient scaling, but also to have negative consequences, including rigidity at the level of code, and perverse or counter-productive incentives at the level of human behaviour, leading to ‘institutional overload’. Proposing an original institutional theory of computational law which identifies the role of governance in ‘scaling and layering’, the paper argues that automated enforcement can only achieve scale effects if human judgement is combined with automation. Human agency is needed to continuously realign and re-fit code-based systems to text-driven laws and social norms in specific spatio-temporal environments. In the final analysis, code operates in a path-dependent and complementary way to these other forms of governance. From social norms to laws, to data and to code, governance is layered via formalisation sustained by human work and societal feedback.