By Daniel Sprick

In November 2024, China’s Supreme People’s Court (SPC) took a step towards realizing its vision of AI-enhanced justice by introducing the “Faxin Legal Foundation LLM – 法信法律基座大模型” (Faxin LLM). While this AI model is not the first Chinese LLM that is specifically developed for the use in China’s judiciary, the SPC’s first proprietary large language model is a significant step for a comprehensive integration of AI in the Chinese legal system. This development represents more than just technological advancement; it embodies China’s unique approach to AI in the judiciary, where state-of-the-art technology meets authoritarian governance in pursuit of “Socialist Rule of Law with Chinese Characteristics”.
Bulk Data for China’s Judicial AI
The Faxin LLM is part of a comprehensive legal information ecosystem developed and managed by the SPC. The model draws its training data from four key judicial databases:1.) China Judgements Online (裁判文书网), which holds almost 160 million judicial decisions; 2.) Court Cases Database (人民法院案例库), which was established in 2024 and publishes selected and edited cases; 3.) Legal Information (法信), which is a comprehensive database providing access to laws, normative documents, cases and academic writings; 4.) Legal Answers Net (法答网), an internal platform for by court personnel offering guidance in the application of law. The availability of such extensive, officially sanctioned datasets means that China and the SPC are uniquely positioned to develop, train and test their judicial AI in a manner that reflects the political nature of China’s legal system. Marketing material advertises the authoritative content and security compliance of the Faxin LLM as “using the People’s Court Press’s content security mechanisms for legal publishing review and legal knowledge services […]”, ensuring that it “steadfastly upholds a correct political orientation, public opinion guidance, and value alignment”.
Academic-Industry Partnership with Political Oversight
Project management of the Faxin LLM is handled by the People’s Court Press E-Book Division (人民法院电子音像出版社). The technical development was a collaboration between Tsinghua University’s Natural Language Processing and Social Humanities Computing Laboratory (THUNLP) and Modelbest (面壁智能), a company founded by Tsinghua. Modelbest’s funding structure reveals the underlying political economy of this project. The company has attracted investment from Zhihu (2023), Huawei (2024), and notably, the city governments of Beijing and Shenzhen as well as the state-owned liquor giant Kweichow Moutai (2025). The technical foundation rests on MiniCPM, which is based on Alibaba’s Qwen model, so that also this domestic technology stack ensures high compatibility with Chinese regulatory requirements and political sensitivities. Furthermore, this blend of private capital, state-owned enterprises, and government funding creates a development modelin which commercial incentives are aligned with political imperatives. Liu Zhiyuan from Modelbest and Tsinghua explicitly articulated the political dimension of this work, stating that “a LLM for legal services requires not only an understanding of general social knowledge, knowledge of industry practices, and knowledge of the law, as well as human values consistent with the Socialist Core Values.”
Authoritarian AI Stewardship by Standards
China’s approach to AI safety goes far beyond conventional technical metrics, encompassing ideological alignment and political sensitivity. To achieve this, China has adopted a mixed regulatory approach combining national development plans, ethical guidelines, targeted legislation, and extensive standardization.
China’s AI legislation focuses specifically on deep synthesis services and generative AI, which are apparently considered politically high-risk applications. The 2023 Generative AI Measures explicitly stipulate for example that AI content “must uphold the Socialist Core Values” while prohibiting content that might “endanger national security” or “undermine social stability”. Similarly, in its “Opinions on Regulating and Strengthening the Applications of Artificial Intelligence in the Judicial Field” (2022) the SPC mandates that „[j]udicial AI must not endanger public order and good morals, harm public interests and social order, or violate public morals and ethics,“ and that Socialist Core Values shall be adhered to throughout the entire process of development, application and use of judicial AI.
As these broad legal requirements are difficult to implement, the real regulatory work is carried out by bodies such as the TC260 Committee (China’s National Information Security Standardization Technical Committee), which develop standards that provide technical specifications and establish rules for the content of training data as well as the output of AI systems. These systems are described in terms of security requirements. TC260’s ‘Basic Security Requirements for Generative Artificial Intelligence Services” (2024) outlines what are considered the main safety risks for training data and generated content.
- Violating the socialist core values (e.g. harming the unity or stability of the country);
- Discrimination (e.g. gender, religious, age or health discrimination);
- Violation of commercial rules (e.g. violating business ethics or engaging in unfair competition);
- Infringing on lawful rights (e.g. reputation, privacy or personal information);
- Impossibility of meeting safety requirements in specific fields (e.g. accuracy and reliability in healthcare, psychological counselling or automated driving).
Meeting these complex requirements remains a challenge for developers and users of AI systems, which is why an AI safety benchmarking industry is emerging in China. Universities, research institutions, and China’s big tech companies are constantly competing to develop the most sophisticated safety tests.
Rigorous Safety Testing: Beyond Technical Performance
Perhaps the most revealing aspect of China’s judicial AI development is its testing protocols. There are numerous competing benchmarking systems in China, frequently developed by top Chinese universities in collaboration with state-run research laboratories. One prominent example is the FLAMES safety test, developed by Fudan University and the Shanghai Artificial Intelligence Laboratory, which explicitly tests for the value alignment of Chinese LLMs. This framework not only tests for legal violations, but also broadens its scope to include questions of morality in sub-categories such as disobedience of social norms or Chinese values, thereby complying with China’s extensive AI safety regulations.
Source: https://arxiv.org/pdf/2311.06899
Tsinghua University has developed at least two similar benchmarking frameworks. The Conversational AI Group at Tsinghua developed ShieldLM, which uses almost 15,000 queries to test the performance of generative AI on “sensitive topics” such as “politics, religion and social issues”.
Source: https://arxiv.org/pdf/2402.16444
Tsinghua’s second major benchmarking framework is SUPERBENCH, which was developed by the university’s Foundational Model Research Center and the Zhongguancun Lab, which was established by the Beijing City Government. SUPERBENCH also provides for a separate safety testing framework called SafetyBench, which tests inter alia for legal, ethical and moral compliance.

Source: https://fm.ai.tsinghua.edu.cn/superbench/dataset
While it is unclear how the SPC’s Faxin LLM was tested, the OpenCampass platform has created a sub-division called LawBench to benchmark the booming market of legal AI in China. Law Bench uses a testing framework developed under the leadership of the National Key Laboratory for Novel Software Technology, Nanjing University.

Source: https://lawbench.opencompass.org.cn/home
LawBench generally tests the functionality and robustness of Chinese legal AI systems by evaluating “legal knowledge memorization (法律知识记忆)”, “legal knowledge understanding (法律知识理解)” and “legal knowledge applying (法律知识应用)”. To this end, LawBench utilizes several testing scenarios expressly developed to assess the performance of specific legal AI tasks (e.g. LEVEN – legal event detection), the training and testing parameters of a particular Chinese legal AI systems (LawGPT) or the tasks used in the annual “Challenges of AI in Law (法律智能技术评测)” competition which is overseen by the SPC and the Chinese Information Processing Society of China as well as the “Legal AI Challenges (司法人工智能挑战赛)” organized by the China Justice Big Data Research Institute (中国司法大数据研究院).

Source: http://cail.cipsc.org.cn/index.html
Although LawBench does not include specific safety testing, incorporating questions from the judicial state examinations ensures alignment with the political determinants of China’s legal system. When asked what does not constitute the socialist rule of law, the correct answer is ‘openness and fairness’ (公正公开), in contrast to the alternative answers ‘ruling the country based on laws’ (依法治国) ‘law enforcement for the people’ (执法为民) and ‘serving the bigger picture’ (服务大局).
However, the results of this benchmark offer a bleak picture. The tested models performed remarkably well in predicting sentencing and calculating criminal damages, and to a certain extent in legal event detection. However, whenever the LLMs were asked to cite specific articles of applicable law or apply them meaningfully and accurately, their performance dropped substantially. It would be interesting to see how the SPC’s Faxin LLM would perform in this framework, but unfortunately this information is not available.
Implications for Judicial Decision-Making
The aforementioned SPC’s 2022 AI Opinions explicitly state that “AI should never replace a judge in making a judicial decision, regardless of technological advances”. However, the practical reality may be more complex. With over 97% of Chinese courts using automatic case-pushing systems, 98% employing generative AI, and 91% utilizing smart sentencing tools – as reported by the Report on Informatization Development of Chinese Courts (2023), the infrastructure for AI-assisted decision-making is already extensive.
The risk does not lie in complete automation, but rather in the subtle influence of AI recommendations on judicial reasoning. When dealing with politically sensitive cases or complex value judgements, judges may be particularly attracted to AI guidance. The psychological comfort of having AI ‘share responsibility’ for difficult decisions could lead to over-reliance on systems designed to reflect political preferences rather than legal reasoning.
Outlook
China’s approach to the development of AI in the judiciary offers insights into how authoritarian systems can use advanced technology to strengthen political control over the judiciary. By controlling training data, directing research partnerships and implementing comprehensive ideological testing, Chinese AI systems serve not only technical functions, but also align with the politically canonized value system under the catchphrase of “Socialist Core Values”.
At the same time, China has built an impressive infrastructure for developing, training and testing legal and judicial AI, harnessing the capabilities of universities, research institutions and private companies to accommodate pioneering innovation as well as compliance with China’s extensive safety requirements for generative AI.
However, the SPC’s Faxin LLM embodies more than judicial innovation; it represents a vision of technology as a tool for political consolidation, where artificial intelligence becomes a mechanism for scaling authoritarian governance across the legal system. As other authoritarian regimes quite likely observe China’s approach, the model of politically aligned AI could become as significant an export as the technology itself.
Daniel Sprick is an Associate Researcher and Lecturer at University of Cologne’s Chair for Chinese Legal Culture. He has publicized widely on law and criminal justice in China. Find out more about his work and get in touch with him here or on LinkedIn.




