---
title: "Zhu Xiaofeng — Who Pays When GenAI Causation Is Unclear? Applying Civil Code Article 1254 by Analogy"
author: "DCC Editorial"
published: 2026-05-28T07:00:00.000Z
url: https://datacompliancechina.com/posts/zhu-xiaofeng-genai-pi-causation-unclear-liability/
description: "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."
tags: ["ai-governance", "genai", "personal-information", "causation", "liability", "commentary"]
laws_cited: ["pipl", "civil-code-personal-info", "genai-services-interim-measures", "personal-info-audit-measures"]
domains: ["ai-governance", "personal-information", "data-security"]
account: "dejyfz"
original_title: "学术｜朱晓峰：生成式人工智能个人信息侵权因果关系不明时的责任认定"
original_author: "朱晓峰 (Zhu Xiaofeng), Central University of Finance and Economics Law School"
original_publication: "《政法论坛》(Tribune of Political Science and Law), Issue 6, 2025; reposted via 数字经济与法治 WeChat Official Account"
original_url: "https://mp.weixin.qq.com/s/V1EbvwB4Ib-fc5j0EgT3Zw"
source_language: "zh"
---
> *Editor's Note — DCC.*
>
> Zhu Xiaofeng's piece in 《政法论坛》(*Tribune of Political Science and
> Law*, a top-tier Chinese law journal) takes on the question every
> GenAI-deploying multinational will eventually face: when a personal-
> information harm arises from a GenAI service, but the specific causal
> link to any one actor (model designer, model provider, model user,
> data provider) cannot be established, who pays? Zhu's answer is
> doctrinally bold and operationally consequential: apply Civil Code
> Article 1254 — the rule originally designed for harm from objects
> falling from buildings of indeterminable origin — by analogy. The
> framework is currently a doctrinal proposal in a top journal; it is
> the kind of proposal that, in Chinese legal practice, often
> crystallizes into judicial-interpretation doctrine and then into
> rulemaking within 18-36 months. Multinationals deploying GenAI in
> China should design liability allocations and indemnities against
> this contemplated framework now.

## The structural problem

Generative AI personal-information torts involve a typical actor cast:

- **Model designer** — built the foundational model
- **Model provider** — operates the GenAI service
- **Model user** — the entity (often a downstream enterprise) deploying the model
- **Data provider** — supplied the training data or in-context data

PIPL Article 69 paragraph 1 establishes presumed fault for PI processors — so once a specific causation link is identified, the actor must prove non-fault to escape liability. Multi-actor liability allocation considers comparative fault and contribution.

**The problem:** in GenAI cases, the specific causation link is structurally hard to establish. Three reasons:

- **Algorithmic black-box** — the model's internal decision pathway is opaque even to the operator.
- **Training-data cleansing** — data has been transformed, aggregated, and stripped of provenance.
- **Interactive learning** — the model's behavior changes through ongoing user interaction, fragmenting causation across actors and time.

Result: the victim cannot prove which specific actor's conduct caused the PI harm. Under the standard "burden on plaintiff to prove causation" rule, the victim recovers nothing. The PIPL's stated purpose — comprehensive PI protection — is structurally defeated.

## Why the existing frameworks don't fix this

Zhu walks through the existing doctrinal candidates and explains why each fails.

### Civil Code Article 1165 (general tort) — fails

The construction-element approach: plaintiff must prove (a) injury, (b) tortious conduct, (c) causation. The plaintiff cannot prove causation in the GenAI black-box context, so the claim fails. Article 1165 cannot accommodate the unclear-causation scenario.

### Civil Code Article 998 (interest balancing) — fails

For non-material personality-rights infringement, Article 998 allows the judge interest-balancing discretion in evaluating culpability. But Article 998's discretion operates within the construction-element framework — it doesn't relax the causation requirement. The plaintiff still has to provide *prima facie* evidence of causation between the conduct and the harm. The Article 998 framework cannot substitute for the missing causation evidence.

### Burden-shifting alone — fails

Some Chinese scholarship has proposed shifting the burden of proving causation to the defendants. Zhu accepts this is a necessary half-step but argues it's insufficient: even with burden-shifting, where multiple potential defendants each independently demonstrate non-causation for their specific conduct, the victim still recovers nothing. The structural problem is not just *who* bears the burden; it is that *no specific defendant* can be identified as the cause.

### Inferring liability from "principal direct tortfeasor" — fails

Where downstream third-party actors are involved, the model-designer / provider / data-provider's contribution is often absorbed into the analysis of the principal direct tortfeasor's act. The exception is *Remsburg v. Docusearch* (US 2003), but the US precedent has been narrowly cabined. Chinese courts have not adopted a comparable framing.

## Zhu's framework: three doctrinal justifications

Zhu argues for an analogical extension of Civil Code Article 1254 (unclear causation in building-falling-object cases). Three doctrinal foundations:

### 1. Communication-safety theory (交往安全)

The principle: whoever creates or maintains a risk to others has an obligation to take all appropriate and reasonable measures to control the risk and prevent its materialization. Applied to GenAI:

- **GenAI model designers and providers** opened and maintain the risk (the GenAI technology cannot operate without large-scale PI processing, and the model's interactive-learning behavior generates ongoing PI risk).
- **GenAI model users** maintain the risk (their use of the technology is what activates the harm potential).
- **Data providers** contribute to the risk (their data supply enables the PI-processing risk surface).

Each is therefore subject to a communication-safety obligation; failure to discharge it grounds liability *even where specific causation is unclear*.

### 2. Gain-and-risk allocation theory

The principle: whoever benefits most from a risk should bear most of the downside. Applied:

- Model designers, providers, users, and data providers benefit substantially from GenAI deployment.
- PI subjects bear the harm cost but receive minimal benefit.

Allocating all downside to the PI subject — on the technicality that specific causation isn't proven — produces an inversion of the gain/risk-allocation justice principle. Better: distribute the harm cost across the actors that benefit.

### 3. Causation-proof difficulty + harm-prevention

PI causation in GenAI is *structurally* difficult to prove, not just fact-specifically difficult. The doctrine should accommodate this. Additionally, requiring potential actors to bear shared liability creates incentives for them to *prevent harm in the first place* — supporting the harm-prevention function of tort law beyond the compensation function.

The combined justification: communication-safety + gain-risk-allocation + structural-causation-difficulty + harm-prevention provide overlapping doctrinal grounds for the unclear-causation rule.

## The proposed rule: Article 1254 by analogy

Civil Code Article 1254 was originally designed for harm from objects dropping or being thrown from buildings: if the specific actor cannot be identified, *all* potentially-causal users of the building must compensate the victim, with judges determining proportional allocation among them.

Zhu's proposed application to GenAI:

**(1) Trigger condition.** Where (a) PI harm has demonstrably occurred, (b) the harm clearly originates from a specific GenAI service or product, and (c) which specific actor's conduct caused the harm cannot be established despite reasonable investigation.

**(2) Liability scope.** Each potentially-causal actor (model designer, provider, user, data provider) compensates the *full* damage to the victim. The victim is not required to litigate among the actors; the actors must absorb the joint exposure.

**(3) Inter-actor allocation.** Among the actors, the allocation is *proportional* (按份关系). Judges determine the specific amounts case-by-case, considering:
- Each actor's role in opening / maintaining the risk
- Each actor's safeguards (or lack thereof)
- Each actor's economic benefit from the risky activity
- Comparative-fault factors

**(4) Escape mechanism.** Actors that affirmatively prove their conduct is not causally connected to the specific harm can be excluded from the liability pool. This preserves the differential-incentive property — actors that invest in safeguards and can demonstrate non-causation are released from joint liability.

## How this connects to the existing framework

Zhu carefully positions the framework as *complementary* to existing PIPL and Civil Code structures, not as a replacement:

- **Where specific causation IS established** — PIPL Article 69 paragraph 1's presumed-fault rule continues to apply.
- **Where multi-actor liability applies** — Article 1170 (joint dangerous conduct) or Article 1171 (joint conduct) continue to apply as appropriate.
- **The Article 1254 analogy applies specifically to** — the structural unclear-causation case where the conventional frameworks systematically under-protect the victim.

This is doctrinally tidy: the proposed rule fills a specific gap without disturbing the broader liability architecture.

## What this tells overseas compliance teams

- **The unclear-causation gap will get filled.** Even if Zhu's specific Article 1254 analogy proposal isn't ultimately adopted, *some* framework will fill the gap — the alternative (continued under-protection of PI victims in GenAI cases) is not politically sustainable as GenAI deployment scales. Multinationals deploying GenAI in China should design liability and indemnity frameworks against the contemplated joint-liability outcome.

- **Joint-liability allocation across the GenAI supply chain becomes the operating norm.** If you are a model designer, model provider, GenAI service user, or data provider in any role for a Chinese-market GenAI service, plan for the scenario where *you become a defendant in a PI case where no single actor can be specifically blamed*. The contemplated framework imposes joint-liability exposure regardless of your specific causal contribution.

- **Demonstrable safeguards and audit-able compliance records are now operationally consequential.** The escape mechanism in Zhu's proposed framework — actors who can prove non-causation are released — creates a differential incentive. Actors that maintain comprehensive PIIA documentation, algorithmic-decision audit logs, training-data provenance documentation, and verifiable safeguard implementations have a defensible exit from the liability pool. Actors that don't, don't.

- **Contractual allocation across the GenAI supply chain becomes essential.** Designers, providers, users, and data providers should contract — explicitly — on liability allocation, indemnification, and cooperation in the event of a joint-defendant scenario. The contracts should anticipate (a) joint-defendant status, (b) information-sharing obligations to support each party's non-causation defense, (c) cost-allocation in joint defense and joint settlement.

- **Insurance becomes structurally important.** The US insurance-and-industry-fund model Zhu references is one operational answer. Multinationals operating GenAI in China should evaluate (a) cyber/tech-liability policies specifically covering joint-liability exposure from unclear-causation PI scenarios, (b) industry-fund or pool arrangements where available, (c) coverage extensions for the comparable-position actors in your GenAI supply chain.

- **The PIIA + audit infrastructure does double duty.** PIIA (PI Impact Assessment) and the [PI Audit Measures](/posts/pipo-vs-dpo-pi-protection-officer-comparison/) regime are not just direct compliance obligations — they are the *evidentiary infrastructure* for proving non-causation in a joint-defendant case. Invest accordingly.

The deeper doctrinal point Zhu's piece signals: Chinese GenAI tort law is, structurally, going to look very different from the US negligence-and-product-liability approach or the EU AI Act / GDPR approach. The Chinese frame will lean on Civil Code architecture, Continental civil-law doctrine, and joint-liability mechanisms in ways that don't translate cleanly into Western analogies. Multinationals that build compliance programs against the *coming* Chinese frame — joint-liability, evidentiary-defense infrastructure, contractual allocation across the supply chain — will operate the regime efficiently. Multinationals that wait for the regime to crystallize will be reverse-engineering liability allocations into already-deployed GenAI services under unfavorable conditions.

---

— *朱晓峰, 生成式人工智能个人信息侵权因果关系不明时的责任认定 (Liability Determination for Generative AI Personal Information Torts Where Causation Is Unclear), 《政法论坛》(*Tribune of Political Science and Law*), Issue 6, 2025; reposted via 数字经济与法治 WeChat Official Account, November 25, 2025. [Original article (Chinese).](https://mp.weixin.qq.com/s/V1EbvwB4Ib-fc5j0EgT3Zw)*

*Not legal advice. The above is DCC's structured summary of Zhu's analysis, with framing for overseas counsel; the three-pillar doctrinal justification and the Civil Code Article 1254 analogy proposal are Zhu's.*