Abstract:
The advent of AI has coincided with unrelated challenges—some old and some new—in studying Chinese law. To a senior generation, AI is the most recent of several revolutions in access and research that have changed the field. AI offers new research tools and attendant opportunities for Chinese and foreign researchers focused on Chinese law. Like most tools, AI is double-edged, increasing capacities but also coming with risks that scholars need to manage. The mixed impact of AI extends beyond scholarship to publication and teaching. At the same time, AI is an increasingly important and fruitful area for research on Chinese law, in itself and in comparative and international context.

Jacques deLisle is the Stephen A. Cozen Professor of Law, Professor of Political Science (secondary appointment), and Director of the Center for the Study of Contemporary China at the University of Pennsylvania, and Chair of the Asia Program at the Foreign Policy Research Institute. His scholarship focuses on China’s engagement with the international order, Taiwan’s international status and cross-Strait relations, law and legal institutions and their relationship to politics and policy in the PRC, legal and political issues in Hong Kong under Chinese rule, and US-China relations. His work has appeared in Journal of Contemporary China, Asia Policy, China Review, Orbis, Administrative Law Review, Hong Kong Law Journal, University of Pennsylvania Journal of International Law, and other academic and policy journals and edited volumes. He is the co-editor of, and contributor to, Chinese Developmentalism in the Global Legal and Economic Order (2026); The Party Leads All: The Role of the Chinese Communist Party in China’s Politics, Governance, Society, Economy, and External Relations (2022), After Engagement: Dilemmas in U.S.-China Security Relations (2021), Taiwan in the Era of Tsai Ing-wen (2021), To Get Rich is Glorious: Challenges Facing China’s Economic Reform and Opening at Forty (2019), China's Global Engagement: Cooperation, Competition, and Influence in the 21st Century (2017), The Internet, Social Media, and a Changing China (2016); Political Changes in Taiwan under Ma Ying-jeou (2014), and China’s Challenges (2014). He is also co-editor-in-chief of the American Journal of Comparative Law, member of the U.S. State Department’s Advisory Council on International Law, a titular member of the International Academy of Comparative Law, past president of the American Association for Chinese Studies, and former Director of Penn’s Center for East Asian Studies.

Abstract:
Clarifying the actual adjudicative subject in criminal justice involving artificial intelligence (AI)—whether judges or machines—helps reveal the internal mechanisms of AI-assisted adjudication, thereby safeguarding the judge’s central role and upholding the fairness and authority of criminal justice. This study adopts a cognitive experimental approach, in which 165 participants were asked to adjudicate multiple dangerous driving cases under varying priming conditions (with or without AI assistance) and intervention conditions (with or without judicial accountability constraints). Experiment I demonstrates that, under conditions of case-handling pressure, judges exhibit a significant tendency to rely on AI, with adjudicative dominance showing a shift toward machine influence. Experiment II indicates that introducing the judicial accountability system as a form of “cognitive intervention” can partially mitigate excessive reliance on AI. However, its overall effect remains limited. Judges’ dependence on AI does not stem merely from inertia, but rather from the combined effects of cost-benefit considerations, algorithmic authority, and other factors. To prevent potential risks posed by AI-dominated adjudication to substantive justice, procedural justice, and judicial authority, it is necessary to further clarify the judge’s principal role and corresponding judicial accountability in criminal adjudication. Institutionalized cognitive intervention mechanisms should be developed to reduce excessive reliance on AI, and ultimately to construct an empirically grounded model of human-machine collaboration in criminal justice.

Lin Xifen is the Associate Dean of KoGuan School of Law and a tenured professor at Shanghai Jiao Tong University (SJTU). He concurrently serves as Deputy Director of the China Institute for Socio-Legal Studies, Director of the Institute of Procedural Law and Judicial System, and Executive Director of the Center for Computational Law and AI Ethics at SJTU. He is also the Executive Associate Editor-in-Chief of SJTU Law Review. He is a Standing Council Member of the China Association of Criminal Procedure Law, a Standing Council Member of the China Association of Criminal Execution Law, a Council Member of the China Association of Procuratorial Science, and a Standing Council Member and Secretary-General of the Computational Law Committee of the China Computer Federation (CCF).

He has conducted systematic and influential research in areas including wrongful convictions and the exclusionary rule of illegal evidence, empirical studies on the operation of procuratorial power and judicial reform, interdisciplinary empirical research in criminal justice, and judicial decision-making in the era of AI. He has published more than 100 articles in leading Chinese journals such as Social Sciences in China (中国社会科学), China Legal Science (中国法学), Chinese Journal of Law (法学研究), and The Jurist (法学家), as well as in international journals including International Review of Economics & Finance, European Journal on Criminal Policy and Research, International Journal of Crime, Law and Justice, Crime, Law and Social Change, Crime & Delinquency, Policing and Society, Policing: A Journal of Policy and Practice, Prison Journal, Asian Journal of Criminology, Asia & Pacific Law Review, Modern China, China Review, China: An International Journal, and Hong Kong Law Journal. He has led more than 10 research projects at the national, ministerial, and provincial levels, funded by the National Social Science Fund of China, the Ministry of Science and Technology, the Ministry of Education, the Supreme People’s Procuratorate, and Shanghai municipality.