Law v Algorithmic Governance: China’s Social Credit Systems and other Data Experiments

Abstract

This dissertation proposes an institutionalist framework and a ‘scaling and layering’ hypothesis to understand the emerging theoretical domain of algorithmic governance. More specifically, the dissertation is concerned with the debate on the relationship between ‘law’ and ‘code’, with law referring here to various accepted or well established forms of text-based legal governance, and code to emerging forms of algorithmic governance, using machine learning and other aspects of artificial intelligence (‘AI’). The dissertation uses China’s Social Credit System (SCS) and other data/code experiments as case studies to test the validity of the proposed framework and hypothesis. The contributions made by the dissertation are twofold: (1) theoretical and (2) empirical. Theoretically, the dissertation provides a rationale for viewing law and algorithmic governance as complements of, rather than substitutes for each other. This rationale is to be found in the inherent trade-off which exists between scaling and layering in complex forms of legal and algorithmic governance across extended geographies and populations. Empirically, the dissertation presents new evidence on China’s SCS, providing a more systematic and realistic picture of its development alongside various data experiments at both local and national level, and using the scaling-layering framework to revealing gains and problems from its mode of operation.