Geopolitechs
New judicial guidance clarifies fault, platform liability and evidentiary burdens, while deliberately avoiding a uniform rule on AI training and copyright.
The Opinions contain 24 provisions covering AI-enabled face swapping, voice cloning, virtual avatars, personal information, infringement involving AI-generated content, discriminatory pricing, autonomous driving, training data, open-source software, and AI-generated evidence.
The primary purpose of the Opinions is to provide courts with a more consistent framework for adjudicating AI-related disputes under China’s existing civil law regime. In particular, they address how liability should be allocated among developers, service providers and users, and how courts should assess duties of care, fault, causation and burdens of proof.
It is important to clarify at the outset that the Opinions do not constitute new AI legislation, nor are they a judicial interpretation. They are issued under the document designation “Fa Fa” (??) and constitute judicial policy and adjudicatory guidance issued by the SPC. In its accompanying Q&A, the SPC expressly noted that China has not yet enacted a dedicated AI law. AI-related disputes must therefore continue to be adjudicated under existing laws, including the Civil Code, Copyright Law, Personal Information Protection Law, Anti-Unfair Competition Law, Consumer Rights Protection Law, Product Quality Law and Civil Procedure Law. The Opinions themselves therefore cannot create new categories of civil rights or liabilities.
Nevertheless, their practical significance is considerable. The Opinions call for cases that may establish important rules or require greater consistency in the application of law to be heard at a higher level where appropriate, while promoting greater consistency through the People’s Courts Case Database and judicial supervision. Standards developed through future landmark AI cases may therefore have a broader impact on companies’ product design, data governance and risk controls.
On the allocation of liability, the Opinions generally retain a fault-based approach. They do not impose strict liability on model developers or service providers for all AI outputs, but nor do they allow companies to avoid liability simply by invoking “technology neutrality” or arguing that the content was generated automatically by a machine. Courts will assess liability in light of the particular use case, the degree of system autonomy, potential risks and their scope of impact, as well as each party’s ability to foresee and control those risks.
This approach both limits and potentially strengthens platform liability. On the one hand, the Opinions do not categorically treat purely online models or applications as “products” for purposes of the Product Quality Law. Strict product liability therefore remains primarily relevant to AI products with a physical form, such as robots and autonomous vehicles. On the other hand, a platform may still be found at fault if it could reasonably foresee a particular type of harm and had the ability to mitigate that risk but failed to take proportionate measures.
Infringement of personality rights illustrates this framework. The Opinions distinguish between infringing content generated by a model itself and content deliberately induced by users through malicious prompts. Users are responsible for their own intentional infringing conduct, while platform liability depends on factors including foreseeability, technical control and the measures already implemented. Where a rights holder provides verified identity information and prima facie evidence of infringement, a platform that fails to take necessary measures—such as preventing the relevant generation, restricting particular instructions or taking action against an account—may also bear liability for additional harm occurring after notification.
Unlike conventional online platforms, where an infringing article, image or hyperlink can simply be removed, a generative model may reproduce similar content following relatively minor changes to a prompt. An important issue for future cases will therefore be how courts determine whether filtering, prompt restrictions, account measures or model updates constitute reasonable and necessary responses. The Opinions do not prescribe a uniform technical standard, leaving this assessment to the circumstances of individual cases.
The evidentiary rules may have the most immediate practical implications for AI companies. In cases involving copyright infringement by generated content, a rights holder generally remains responsible for producing prima facie evidence that the disputed content was generated by the model in question and is substantially similar to the protected work. Courts may then, where necessary, require the model developer or service provider to explain the sources of its training data, the training process, the operation of the model and the relevant scientific basis.
This does not amount to a wholesale reversal of the burden of proof. Rather, evidentiary burdens are allocated according to access to information: rights holders establish external facts reasonably accessible to them, while parties controlling the model, training materials and operational records may be required to provide reasonable explanations concerning internal facts. Where a party controlling documentary or electronic evidence refuses to produce it without justification, the court may draw adverse factual inferences.
As a result, information concerning training-data provenance, model versions, prompt records, retrieval-augmented generation (RAG) materials, filtering rules, risk testing and complaint handling may evolve from internal R&D records into important litigation evidence for determining infringement, fault and causation. AI compliance is therefore increasingly moving from the question of whether an obligation was fulfilled to whether the company can demonstrate that it was fulfilled.
Read full article ( a lot more detail to come)
https://www.geopolitechs.org/p/chinas-supreme-court-sets-the-rules
