Rule by Algorithm

Michelle Miao*

Algorithms are not just augmenting authoritarian control—they are revolutionizing it. Using contemporary China as a case study, this Article argues that rule by algorithm has emerged as a new mode of governance in authoritarian contexts. Contrary to liberal democracies’ anticipation that, with growing legal norms, institutions, and consciousness, China would transform into a full-fledged rule-of-law society, the country has instead gravitated towards a society governed by algorithms, exemplifying a new form of algorithmic authoritarianism. To illustrate this paradigm shift, this study focuses on two emerging algorithmic regimes: health Quick Response (QR) codes and social credit scores. These systems serve to bolster the legitimacy of and strengthen control by the Party-state. This research demonstrates that rule by algorithm offers three comparative advantages over traditional rule of law: first, enhanced bureaucratic control; second, granular surveillance and behavior modification; and, third, ideological neutralization through data-driven governance. Leveraging technological rationality enables authoritarian regimes to simultaneously maximize administrative efficiency and circumvent the traditional rule-of-law institutional constraints on political control. Thus, rule by algorithm represents a malleable, subtle, and effective model for sustaining authoritarian rule, potentially reshaping the global landscape of governance in the digital age.

* Associate Professor, Faculty of Law, the Chinese University of Hong Kong (CUHK); Fellow (2023–24), Center for Advanced Study in the Behavioral Sciences (CASBS), Stanford University. The author would like to acknowledge the sponsorship from CASBS and CUHK for this project. The author is deeply indebted to the invaluable comments and feedback provided by Professor William P. Alford, Professor Sarah A. Soule, Professor Ralph Schroeder, Professor Gideon Yaffe, Professor Robert J. MacCoun, Professor Lisa Blaydes, Professor Robert O. Keohane, Professor Lucas Bessire, Professor Erica Robles-Anderson, Professor Andrew Penner, Professor Stefan Link, Professor Barbara Keys, Professor Young Mie Kim, Professor Elizabeth R. DeSombre, Professor Santi Furnari, Professor David S. Moore, Professor Adel Daoud, Professor Conor Mayo-Wilson, Professor Ceren Budak, Professor Emily Penner, Professor Samuel Barkin, Professor Gabrielle Clark, Professor Dawn Moore, Mr. Jeffrey L. Bleich, Mr. Stéphan Vincent-Lancrin, Mr. Zachary Ugolnik, and all members of the CASBS community. Their insights and support have been invaluable in shaping this work. Any errors or omissions remain the sole responsibility of the author.