China’s Approach to AI Liability: Can Fault-Based Liability Keep Pace with AI?

China’s Supreme People’s Court (SPC) issued guidance on handling disputes involving artificial intelligence on 7 September 2026. The guidance provides a framework for applying existing laws to AI-related disputes, particularly where AI systems have caused harm. It addresses personality rights, the processing of personal data, intellectual property, consumer protection and AI-generated content.

Applying Existing Laws to AI

In my view, the most significant aspect of this guidance is not the specific liability rules themselves, but the use of judicial guidance to address AI-related disputes while China has not yet enacted a dedicated AI law.

AI technology is developing at a pace that often exceeds the speed of legislative processes. A comprehensive AI law may require frequent amendments as new technologies, risks and use cases emerge. Guidance that helps courts apply existing legal principles can provide greater flexibility while still promoting consistency in decision-making.

This does not mean judicial guidance replaces future AI legislation. Rather, it provides courts with a framework for applying the laws already in place.

The SPC’s approach appears to recognise that AI governance remains an evolving area. Rather than attempting to create rules for every possible future scenario, the guidance focuses on how existing legal concepts, such as fault and foreseeability, should be applied in an AI context. This allows courts to adapt their analysis as technology develops without requiring lawmakers to constantly revise legislation.

Fault-Based Liability and Its Exceptions

Another notable feature is the guidance’s clarification that fault-based liability applies to AI-related harms where existing law does not expressly provide for no-fault liability or presumed-fault liability. It is therefore a general approach, rather than a rule that applies to every AI-related claim.

When assessing fault, courts should consider factors including the specific application of the AI system, its degree of autonomy, technological and informational transparency, potential risks and their scope, preventative measures, and the relevant parties’ ability to foresee and control harm.

In my view, this reflects a balanced approach. It seeks to hold organisations accountable where appropriate while avoiding excessive liability that could discourage innovation.

Can Fault-Based Liability Keep Pace With AI?

However, the practical application of fault-based liability may present challenges. One of these is the increasing complexity of modern AI systems. Some AI technologies, particularly those designed to adapt through ongoing interactions and feedback, may evolve in ways that are not entirely predictable.

While developers are expected to identify foreseeable risks and implement appropriate safeguards, there may be circumstances where harmful outcomes arise despite reasonable testing and governance measures.

The guidance itself identifies technological transparency, the ability to foresee harm and the capacity to control risks as relevant factors when assessing fault. However, where an AI system produces an unexpected outcome based on patterns learned from large volumes of data, establishing whether a developer, provider or user acted negligently may become significantly more difficult.

In such cases, courts may need to assess not only what harm occurred, but also whether the organisation took reasonable steps to identify and manage risks beforehand.

The distinction between an unforeseeable AI outcome and a failure to implement appropriate controls may become one of the most important issues in future AI litigation.

Why Governance and Evidence Matter

The guidance also highlights the importance of evidence. For example, in certain copyright disputes, developers defending an infringement claim may be required to provide information about training data sources, training records and how the model operates.

More broadly, organisations should consider maintaining appropriate records relating to training data, model development, testing processes, risk assessments and complaint handling. These records may become important evidence of the measures taken to prevent harm and comply with applicable legal obligations.

This is a practical governance consideration, rather than a blanket record-keeping requirement imposed by the guidance for every AI system.

An Evolving Approach to AI Liability

Overall, China’s approach offers an interesting example of using judicial guidance alongside existing legislation to address AI-related disputes. It provides a framework that can develop through judicial practice while promoting greater consistency for courts and organisations.

That being said, future cases may test the limits of fault-based liability where AI systems behave in ways that are difficult to predict or explain.

As AI technology becomes increasingly sophisticated, courts may need to further develop principles around foreseeability, accountability and the standard of care expected from developers and deployers of AI systems.

Source: Supreme People’s Court guidance on AI-related disputes, published 7 September 2026 (Chinese-language original).