[Editor to fill: 200-word domain overview.]
§ DOMAIN · AI GOVERNANCE
AI Governance.
人工智能治理
Rules and standards for generative AI services, deep synthesis, content labeling, and AI ethics in China.
The legal corpus.
6 laws.
- RULE Interim Measures for the Management of Generative Artificial Intelligence Services 生成式人工智能服务管理暂行办法
- RULE Provisions on the Administration of Algorithmic Recommendation Services for Internet Information Services 互联网信息服务算法推荐管理规定
- RULE Provisions on the Administration of Deep Synthesis of Internet Information Services 互联网信息服务深度合成管理规定
- RULE Measures for the Labeling of AI-Generated and Composed Content 人工智能生成合成内容标识办法
- RULE Interim Measures for the Management of AI Anthropomorphic Interaction Services 人工智能拟人化互动服务管理暂行办法
- FRT Measures Administrative Measures for the Application Security of Facial Recognition Technology 人脸识别技术应用安全管理办法
In this domain.
7 briefs.
- § 01 · AI-GOVERNANCE
Zhu Xiaofeng — Who Pays When GenAI Causation Is Unclear? Applying Civil Code Article 1254 by Analogy
Zhu Xiaofeng (Central University of Finance and Economics Law School) takes on the GenAI causation black hole — when a personal-information harm clearly arises from a GenAI service but specific causation among model designer, model provider, model user, and data provider cannot be established, who pays? Zhu's structural answer: when conventional construction-element-analysis and Article 998 interest-balancing both fail (and they do), apply Civil Code Article 1254's 'unclear-causation' rule by analogy — the same rule used for falling-object-from-building cases. The doctrinal scaffolding: communication-safety theory, gain-and-risk allocation theory, causation proof + harm prevention. Critically: each potential injurer compensates the full damage; among themselves, allocation is proportional, with judges determining specific amounts case-by-case. Highly relevant for multinationals deploying GenAI in China — the proposed framework restructures the operating liability surface.
- § 02 · AI-AGENTS
Mapping the AI Agent Risk Surface — A Ten-Category Taxonomy Under China's New 智能体新规
China's Cyberspace Administration jointly issued the Implementation Opinions on Standardized Application and Innovation Development of AI Agents (the '智能体新规' or 'Agent Rules') on May 8, 2026 — the first dedicated regulatory document on AI agents anywhere in the world. This DCC brief works through the ten-category risk taxonomy that practitioners are now using to map the agent attack surface: goal hijacking, tool misuse, identity/permission abuse, supply-chain compromise, unintended code execution, memory and context poisoning, inter-agent communication insecurity, cascade failures, human-machine trust exploitation, and rogue agents. With the agent risk mapped, the brief works the legal-liability vector: how each risk maps to administrative, civil, and criminal exposure under existing PIPL, CSL, Anti-Unfair Competition, and trade-secret regimes. Closes with the Guangzhou Internet Court's recent dual-authorization ruling against an open-source agent that bypassed a chat platform's risk controls — the first Chinese case to articulate the dual-authorization principle for AI agents accessing third-party platforms.
- § 03 · AI-AGENTS
Operationalizing AI Agent Governance — A Ten-Step Internal Control Framework
Part 2 of DCC's brief on the Chinese Agent Rules (《智能体规范应用与创新发展实施意见》, May 2026). After mapping the ten-category risk taxonomy in Part 1, this brief works through the ten-step internal governance framework practitioners are now building to operationalize agent compliance: cross-functional governance organization + agent asset inventory; use-case admission and classification (L1 read-only / L2 limited-write / L3 sensitive-data / L4 high-impact); security assessment and AI red-team testing; identity authorization and permission control (with the under-discussed 'permission inheritance' trap); data protection; tool and protocol security; human-in-the-loop design; supply-chain security; continuous monitoring; and AI-specific incident response. Closes with five operational priorities for teams that need to start now without waiting for the 'big-and-comprehensive' regime build.
- § 04 · AI-GOVERNANCE
Open-Source Does Not Mean Open Data — Zhang Ping on Training-Data Compliance for Open-Source AI
Peking University Law School professor Zhang Ping, writing in 人民论坛 (People's Tribune), takes apart two misconceptions that have dominated the Chinese open-source AI discussion: that 'open source' means training data has no copyright protection, and that 'algorithm open-source' compels 'training data publication.' Both false. Zhang lays out the structural distinction: 'open source is conditional authorization under license' — applied to model weights, not to the training corpus, which is a legally independent object. She then maps the full-chain compliance risk (acquisition / processing / output) and proposes a four-tier differentiated governance framework that finance, healthcare, and government AI deployments can actually use to map their training-data inventory against compliance gates.
- § 05 · FOREIGN-INVESTMENT-SECURITY-REVIEW
Why China Used Foreign Investment Security Review on Manus — Not Tech or Data Export
Hong Yanqing on Beijing's banning of Meta's Manus acquisition. The regulator's choice of pathway — Foreign Investment Security Review, not Technology or Data Export — signals a shift from 'transaction-level' to 'capability-level' oversight of frontier AI projects, with implications for any overseas tech investment touching China.
- § 06 · TOKENS
Cold Water on 'Token Trading' — Wang Qinglan on the NDA's High-Quality Data Set Initiative
In March 2026, the National Data Administration released the *Implementation Plan for Promoting High-Quality Industry Data Set Construction (Draft for Public Consultation)*, which explores a 'token (词元) based value system' and 'token trading as a new transaction mode' for high-quality data sets. The Chinese AI policy community immediately heralded the move as 'revolutionizing data trading.' Wang Qinglan pours cold water: token is a measuring unit, not a magic transformer. AI tokens are not crypto tokens. The bottleneck in China's data-element market isn't measurement — it's supply, rights clarity, compliance cost, and data silos.
- § 07 · FACIAL-RECOGNITION
When Is Facial Recognition in a Public Place 'Necessary for Public Security'? Hong Yanqing's Four-Element Framework
Hong Yanqing on how to operationalize PIPL Article 26's 'necessary for public security' principle for public-place video surveillance and facial recognition. His framework: a four-step necessity test, tiered risk regime with a published prohibited list, three-fold technical controls, and a lifecycle closure mechanism — drawing on EU AI Act and US state-level practice.