Filed under burden-of-proof
Every brief tagged "burden-of-proof".
- § 01 · AI-AGENTS
Fault for Services, Strict Liability Only for Machines: Wang Liming on Tort Liability When AI Agents Cause Harm
In Tsinghua University Law Journal (《清华法学》) 2026 No. 5, Wang Liming (王利明) of Renmin University sets out how Chinese tort law should handle harm done by AI agents. His answers: an agent is never a liable party, so liability falls on the people along its value chain; AI that provides a service, such as generative AI, is judged by fault, while strict product liability applies only to agents that enter the physical world as tangible things and threaten life, body, health or tangible property; where fault applies, operators and deployers should prove they took reasonable care; multiple actors are not jointly and severally liable by default, but answer in proportion to their causal contribution, with the party closest to the end user carrying the heaviest risk-control duty; open-source and free providers get lighter treatment; causation should be presumed when the evidence sits with the defendant; and the 'mass micro-torts' agents produce, such as leaks of many people's personal information, call for representative and public-interest litigation rather than individual suits. DCC summarizes the article section by section and maps each proposal against the Supreme People's Court's AI Disputes Opinions (法发〔2026〕10号), the PIPL and the Civil Code. Most of Wang's framework is already in the Opinions; the reversal of the burden of proof for service agents, the presumption of causation, and substitute measures for data-subject rights go further.
- § 02 · TRADE-SECRETS
When You Cannot Compare the Code: The SPC's RMB 166 Million Centrifuge Case and How to Prove Data Misappropriation Without the Defendant's Data
AnJie Broad partner Li Yanying reads (2022)最高法知民终1592号, the 'centrifuge case' — selected as one of the SPC's 2025 typical anti-unfair-competition cases on 8 September 2025 — for what it teaches about the burden of proof when the misappropriated asset is data. The plaintiffs never obtained the defendants' software or dataset, never decompiled anything, and never ran a substantial-similarity comparison. Instead they exploited the input-output character of software: enter the claimed impeller basic-stage data into their own program along with performance figures taken from the defendants' published product literature, and the output matched. Under AUCL Article 39(2)(i) that was enough to shift the burden, and the defendants' refusal to permit on-site inspection or produce design files converted into an adverse inference. DCC adds the correction the commentary omits: the SPC did not protect the software as a trade secret at all — the plaintiffs failed to delimit its secret points, so it was rejected on the not-generally-known element, and only the dataset was protected. The software was the measuring instrument, not the asset. For overseas counsel holding datasets in China, this is the enforcement route that actually carries damages, and the case is a lesson in how to delimit a claim before you file.