Filed under genai
Every brief tagged "genai".
- § 01 · AI-GOVERNANCE
China's AI-Companion Rule Takes Effect July 15 — A Clause-by-Clause Field Guide to What Actually Changed
China's Interim Measures for AI Anthropomorphic Interaction Services (人工智能拟人化互动服务管理暂行办法) — the world's first dedicated rule on 'companion'-style AI — take effect on 15 July 2026. This DCC brief synthesises three Chinese-language readings published in the days before the effective date: 数据合规肖大国's article-by-article practitioner walkthrough, 网安寻路人 (Hong Yanqing)'s multi-part work on how to scope anthropomorphic interaction (including his 'Sentiment Interaction Event / SIE' indicator system), and AI前沿信息笔记's read of the business-model logic the rule is really aimed at. Three throughlines: (1) what changed between the consultation draft and the final text — real fines were added, a 'continuity (持续性)' qualifier now narrows scope, the emergency-contact duty was widened beyond vulnerable groups, and the mandatory 'human takeover' of at-risk conversations was dropped; (2) the scope question the rule leaves under-specified — which services are 'continuous emotional interaction' at all — and the SIE-style indicator approach practitioners are reaching for to answer it; and (3) the paradigm shift the rule marks, from *content-safety* governance (AI as tool) to *relationship* governance (AI as social role), which finally gives regulators a handle on attention-economy and emotional-dependency business models. For overseas counsel shipping companion, emotional-AI or character-AI products into China: this is the operational checklist and the open-question list, two weeks out.
- § 02 · AI-AGENTS
TC260's Practice Guide on AI-Agent Deployment: A Five-Stage Lifecycle Checklist, Read Against PIPL, DSL, and CSL Obligations
On July 1, 2026 the National Cybersecurity Standardization Technical Committee (TC260) issued the Cybersecurity Standards Practice Guide — Security Guidelines for the Deployment and Use of AI Agents (网络安全标准实践指南——智能体部署使用安全指引), covering the full lifecycle of high-permission, LLM-based personal-assistant agents across five stages: assessment, preparation, deployment, use, and decommissioning, plus a star-rated security checklist (Appendix A) and an organizational management framework including shadow-agent discovery (Appendix B). This DCC brief adapts the HexCode reading published on 数据何规 — itself generated, the account notes, by its own AI agent — which maps each stage onto hard-law anchors: PIPIA duties under PIPL Article 55 and DSL Article 27 risk monitoring at assessment; the GenAI Measures' filed-model requirement and the ban on unverified API relays at preparation; least privilege, directory isolation, CSL Article 21 log retention, and high-risk-operation confirmation lists at deployment; minimum-necessary provision of personal information and long-term-memory management in use; and credential revocation and data disposal at decommissioning. Practice guides are soft law — but in Chinese enforcement practice they calibrate what 'necessary measures' means, and this one is the first lifecycle baseline for the agent era.
- § 03 · AI-GOVERNANCE
China's First 'AI Hallucination' Tort Judgment — GenAI Is a Service, Not a Product, and the Chatbot's '¥100,000 Promise' Binds No One
The Hangzhou Internet Court has decided China's first 'AI hallucination' (AI幻觉) tort case — written into the Supreme People's Court's 2026 work report to the NPC. A user asking a chatbot about college applications was told, across seven rounds, that a non-existent campus existed; when finally shown the official website, the model 'apologised' and 'promised' to pay ¥100,000, even generating a fake lawsuit template telling him to sue. He did. The court dismissed every claim and, in doing so, laid down the first judicial articulation of China's generative-AI liability framework: (1) an AI model is not a civil subject, so its 'promise' is no declaration of intent — and is not attributable to the provider either; (2) generative AI is a service, not a product, so fault liability under Civil Code Article 1165 applies, not product liability's no-fault rule under Article 1202; (3) there is no result-based duty to guarantee accuracy for ordinary inaccurate output — only a process duty of care (conspicuous AI-content labelling plus industry-standard accuracy measures), which the provider had discharged; and (4) no proven damage, no causation. For any company deploying GenAI to the Chinese public, this is the operating liability surface and the evidentiary playbook.
- § 04 · AI-GOVERNANCE
Prompt Stacks and Prompt Governance — Why System-Level Prompts Are Emerging as a Regulatory Lever (and Where They Fall Short)
A Chinese AI-law reading of Neumann, Sargeant and Singh's FAccT 2026 paper Prompt Governance? — and what it means for how China, the EU, and the US treat 'system prompts' as a regulatory object. Li Wenlong (科技利维坦) walks through the four-layer 'prompt stack' (system instructions → system guidelines → developer instructions → user prompts), five properties practitioners need to understand (layered, hidden, natural-language, malleable, loosely coupled to behaviour), and the comparative regulatory landscape: the EU GPAI Code of Practice requires signatories to disclose system prompts to regulators in model reports; the Trump EO 14319 / OMB M-26-04 stops at model / system / data cards and leaves system-prompt disclosure voluntary; the UK's AI Cybersecurity Code says effectively nothing. China's current GenAI safety regime (TC260-003 plus the GenAI Interim Measures) is output-evaluation-based — filing and pre-launch scoring, with no architectural hook into system prompts. Li predicts a Brussels Effect: system-prompt disclosure to regulators will become a global compliance baseline, analogous to the DPIA in data law. For overseas counsel: this is what is coming, what to start archiving now, and why 'what you write' in a system prompt is not 'what the model executes.'
- § 05 · 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.
- § 06 · 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.