Generative AI and Algorithms Lawyers in China

A China generative-AI review should begin with the actual service and deployment rather than the model label. The business should distinguish public-facing generative services from enterprise or internal tools, model provider from application provider and technical supporter, and generation from recommendation or deep synthesis. Counsel should map users and geography, training and optimization data, prompts and outputs, personal information, intellectual property and confidential material, content risks, product and sector uses, algorithm filing and security-assessment triggers, generated-content labelling, user terms and complaint channels, model evaluation, human oversight, incident response and material changes. A hosted foreign model, open-source component or API does not by itself transfer the provider’s China-facing responsibilities.

Questions that change the route

What service is offered, to whom, and by which entities?

Describe public, enterprise, professional or internal access; mainland users and technical infrastructure; model, fine-tuning, retrieval, agent and application layers; providers, deployers and vendors; generation, recommendation and deep-synthesis functions; sector-specific uses; monetization, launch date and geographic controls.

What data, rights, and content enter and leave the system?

Inventory pre-training and optimization corpora, licences and provenance, personal and sensitive information, user prompts, retrieval sources, uploaded files, feedback, model and system logs, generated text, image, audio, video and code, retention, access, cross-border paths, opt-outs, deletion and rights-handling.

Which pre-launch and operational controls are evidenced?

Test algorithm filing and security assessment, generated-content explicit and implicit labels, user identification where applicable, terms and notices, prohibited-content controls, bias and discrimination testing, accuracy and reliability measures, IP and personality-right safeguards, model red-teaming, monitoring, complaint handling, takedown, incident escalation and material-change review.

Evidence to organize first

• Entity, service, user, geography, infrastructure, model and vendor architecture; product requirements and release history; applicability analysis for generative AI, algorithm recommendation, deep synthesis, telecommunications, publishing and regulated-sector rules
• Training, fine-tuning, retrieval, prompt, output and log data maps; provenance, licence and permission records; personal-information analysis and impact assessments; vendor agreements, cross-border mechanism, retention and deletion controls
• Algorithm filing and assessment records; model and content test plans and results; labelling implementation; user terms, notices and complaints; access, monitoring and human-review logs; incident, takedown, remediation and change-management files

Official orientation sources: Interim Measures for Generative Artificial Intelligence Services · Provisions on Algorithmic Recommendations in Internet Information Services · Measures for Labelling AI-Generated and Synthetic Content. Source check: 8 October 2026.