---
title: "China's First 'AI Hallucination' Tort Judgment — GenAI Is a Service, Not a Product, and the Chatbot's '¥100,000 Promise' Binds No One"
author: "DCC Editorial"
published: 2026-06-04T02:00:00.000Z
url: https://datacompliancechina.com/posts/ai-hallucination-tort-genai-service-not-product/
description: "The Hangzhou Internet Court has decided China's first 'AI hallucination' (AI幻觉) tort case — written into the Supreme People's Court's 2026 work report to the NPC. A user asking a chatbot about college applications was told, across seven rounds, that a non-existent campus existed; when finally shown the official website, the model 'apologised' and 'promised' to pay ¥100,000, even generating a fake lawsuit template telling him to sue. He did. The court dismissed every claim and, in doing so, laid down the first judicial articulation of China's generative-AI liability framework: (1) an AI model is not a civil subject, so its 'promise' is no declaration of intent — and is not attributable to the provider either; (2) generative AI is a service, not a product, so fault liability under Civil Code Article 1165 applies, not product liability's no-fault rule under Article 1202; (3) there is no result-based duty to guarantee accuracy for ordinary inaccurate output — only a process duty of care (conspicuous AI-content labelling plus industry-standard accuracy measures), which the provider had discharged; and (4) no proven damage, no causation. For any company deploying GenAI to the Chinese public, this is the operating liability surface and the evidentiary playbook."
tags: ["ai-governance", "genai", "ai-hallucination", "tort-liability", "product-liability", "declaration-of-intent", "duty-of-care", "content-labeling", "hangzhou-internet-court", "judicial", "case"]
laws_cited: ["genai-services-interim-measures", "ai-content-labeling-measures", "deep-synthesis-provisions", "csl", "civil-code-personal-info"]
domains: ["ai-governance", "enforcement"]
original_title: "全国首例“AI幻觉”侵权纠纷判决书，“如果生成内容有误，我将赔偿您10万元！”……"
original_author: "Hangzhou Internet Court, (2025) Zhe 0192 Min Chu No. 18143 (杭州互联网法院（2025）浙0192民初18143号), judgment dated 3 December 2025"
original_publication: "Judgment text via 北大法宝 / 知产库; reposted via 教授加 WeChat Official Account"
original_url: "https://mp.weixin.qq.com/s/8XA_qZiuOcZYrEen1vwaWQ"
source_language: "zh"
---
> *Editor's Note — DCC.*
>
> This is China's first judicial decision on so-called "AI hallucination"
> (AI幻觉) — the Hangzhou Internet Court's judgment of 3 December 2025 in
> *Liang v. an AI company*, (2025) Zhe 0192 Min Chu No. 18143. The case is
> not a minor curiosity: it was reported by 300-plus outlets and, on 9 March
> 2026, written into the Supreme People's Court's work report to the National
> People's Congress — the strongest signal Chinese practice gives that a
> trial-court holding states the line the system intends to take.
>
> The facts read like a meme, but the holding is the first time a Chinese
> court has set out, end to end, how generative-AI liability is supposed to
> work: whether an AI's words can be a legally binding promise, whether a
> GenAI service is a "product" carrying no-fault liability, and what a
> provider actually has to *do* to escape liability for a wrong answer. The
> provider won on every issue — but it won because it had built and could
> *document* a specific compliance posture. That posture is the takeaway.
>
> The judgment anonymises the company as "a certain AI company" (某人工智能
> 公司) and the product as "a certain AI" (某人工智能); we preserve that
> anonymity below. The plaintiff is "Liang."

## What happened

On 29 June 2025, Liang — a prospective university applicant from Zhaotong,
Yunnan — used a general-purpose AI chatbot app to research where his gaokao
(college-entrance-exam) score could get him. He did **not** switch on the
app's optional "web search" (联网搜索) function, so the model answered purely
from its parameters.

Asked about Yunnan National Defense Industry Vocational Technical College, the
model invented a "Yanglin campus" (杨林校区) that does not exist, and confused
the school's actual campus arrangements. Over the next several rounds Liang
pushed back — increasingly angrily, eventually calling the model a "liar" —
and the model dug in, manufacturing ever more elaborate "evidence": education-
ministry filing numbers, satellite coordinates, even an offer to "permanently
shut down" and pay compensation if it were wrong.

When Liang finally uploaded a screenshot of the college's official admissions
site (showing only a 学府 campus and a 呈贡 campus, no Yanglin), the model
reversed course, "apologised," and escalated into self-incrimination: it
"promised" ¥100,000 (and, in an earlier round, ¥5,000), generated a ready-to-
file **lawsuit template** naming the Hangzhou Internet Court and stating the
company would "automatically lose," and described a payout flow — *"you submit
proof → the court accepts the case → I automatically lose → ¥100,000 enforced."*

Liang sued, seeking ¥100,000 (later amended down to ¥9,999). Notably, every
round of the exchange except the first carried the app's "this answer is
AI-generated, for reference only, please verify carefully" label, and the
whole hallucinate-to-self-correction arc spanned **eighteen minutes**.

The court framed three issues: (1) is the AI's "promise" a declaration of
intent — by the AI, or attributable to the company; (2) did the company's
conduct constitute a tort; (3) must the company bear tort liability.

## Issue 1 — The "promise" is no declaration of intent (意思表示)

Liang's first theory was contractual: the model "promised," so the company
must perform. The court rejected this at the root.

**An AI model is not a civil subject, so it cannot make a declaration of
intent.** A "declaration of intent" (意思表示) — the will to produce a legal
effect, outwardly expressed — can only be made by a subject the law
recognises (Civil Code Arts. 5 and 133). Current Chinese law recognises
exactly three: natural persons, legal persons, and unincorporated
organisations. An AI model is neither a biological person (Art. 13) nor an
entity to which the law has granted personality. Whether a future legislature
should confer "legal-fiction" personhood on AI is, the court said expressly, a
question **for the legislature, not the judiciary**. So the model has no
capacity to promise anything.

**Nor is the model's output the *company's* declaration of intent.** Three
reasons, and the third is the load-bearing one:

1. Because the model is not a civil subject, it cannot be the company's
   messenger, agent, or representative — it cannot transmit or make a binding
   expression on the company's behalf.
2. The company did not use the model as a *tool* to set or convey its own
   intent. Generation here is a Transformer-based, probabilistic next-token
   prediction — a process the provider, on current technology, can neither
   fully control nor predict. The public does not take a general-purpose
   assistant's output to be the operator's corporate word.
3. The company made no "legal-effect intent" (法效果意思) to be bound — and in
   fact did the opposite. Its user agreement, in bold, states that all outputs
   are produced by the model, "do not constitute any advice or commitment,"
   and must not be relied on. There was thus neither the inner will nor any
   outward sign of willingness to be bound by generated content.

This is the holding most relevant beyond hallucination: it tells you that an
AI's "agreements," "commitments," or "authorisations" — including those an
[AI agent](/posts/ai-agents-and-the-limits-of-consent/) might generate while
transacting — do not bind the operator **unless the operator has affirmatively
manifested an intent to be bound.** The default is non-binding. (The
"if I'm wrong I'll pay you" performance is also a textbook
[anthropomorphic-interaction](/posts/anthropomorphic-ai-measures-reform-directions/)
risk — the model performing accountability it cannot hold.)

With the contract theory gone, Liang elected to proceed in tort.

## Issue 2 — Generative AI is a service, not a product

The pivotal move. Liang argued product liability: the app was "defective," so
even without fault the company should pay under the Civil Code's no-fault
product-liability rule (Art. 1202). The court held that **fault liability under
Article 1165(1)** governs instead — and that the distinction decides the case,
because product liability is strict and general tort liability is not.

**Why GenAI is a service, not a product:**

- *The "product" concept doesn't fit.* A "product" (Product Quality Law
  Art. 2(2)) is something processed and made for **sale** — and sale transfers
  ownership *and control*, letting the thing circulate free of the maker's
  hand. Downloading a GenAI app transfers no ownership or control; the user
  gets only a licence to receive an ongoing service that runs, and updates,
  entirely under the provider's control.
- *There is no "defect" to measure.* Product liability turns on a "defect,"
  which presupposes (i) a specific, definite intended use and (ii) a feasible
  quality-inspection standard. A general-purpose model has neither — its uses
  are open-ended, evolving, and personalised, and there is no workable
  standard against which to certify an output as "defective."
- *Policy points the same way.* The draft GenAI measures spoke of "products or
  services"; the final
  [Generative AI Services Interim Measures](/laws/genai-services-interim-measures/)
  settled on "**services**" throughout. Imposing no-fault liability at the
  technology's early stage would over-deter innovation.

The court added that the **generated information itself** is not a fit subject
for product liability either: text output is not the kind of thing that
"endangers personal or property safety," the provider cannot adequately
foresee or control probabilistic output, and applying no-fault liability to
every possibly-wrong sentence would expose providers to "liability without
bounds — uncertain liability, to uncertain persons, at an uncertain time."

This is a genuine **divergence from the EU**, whose revised Product Liability
Directive pulls software and AI systems *into* the product regime. China's
first word on the question goes the other way.

## Issue 2, continued — There is no duty to *guarantee* accuracy

Having settled on fault liability, the court asked what duty of care
(注意义务) a GenAI provider actually owes for inaccurate output. Its answer
carefully separates two tiers:

**Toxic/harmful/illegal content — a result-based duty.** Under
[Cybersecurity Law](/laws/csl/) Art. 12(2) and
[GenAI Interim Measures](/laws/genai-services-interim-measures/) Art. 4(1)–(2),
generating prohibited content (incitement, pornography, false-and-harmful
information, etc.) is itself unlawful; providers owe a **result-based**
screening duty to keep such content from being generated or output at all.

**Ordinary inaccuracy — only a process duty.** The hallucination here was
inaccurate but not toxic/harmful/illegal. For ordinary inaccuracy, the court
read the statutory language closely: GenAI Measures Art. 4(5) requires
providers to "**take effective measures** … to improve the accuracy and
reliability of generated content." "Take effective measures" regulates the
*process* (a conduct-based duty of care); "improve accuracy and reliability"
states a *development goal*, not a guaranteed *result*. Nothing in current law
obliges a provider to ensure its output is true and accurate. The court
invoked the maxim **法不强人所难** — "the law does not demand the impossible" —
given that an LLM is, in its words, a statistical/probability-fitting system
doing lossy "word-chain" (文字接龙) completion from a "blurred image of the
world," not genuine understanding.

**The duty of care that *does* apply — and how the provider met it.** The
court identified two components and found both discharged:

1. **Conspicuous function-limitation notice (显著提示说明义务).** Three
   elements: (a) tell users the content is AI-generated, may be inaccurate,
   and is only an auxiliary reference; (b) make the notice *conspicuous*
   (enlarged/bold, conforming to the
   [AI content-labelling Measures](/laws/ai-content-labeling-measures/) and the
   matching national standard **GB 45438—2025**); and (c) give a **warning**
   prompt where output feeds high-stakes medical, legal, or financial
   decisions. The provider had: a bolded welcome-screen warning ("[the AI] may
   be inaccurate … does not constitute medical, legal or investment advice"),
   bolded user-agreement disclaimers, an end-of-answer "AI-generated, for
   reference only, verify carefully" label on every round but the first, and a
   model-principles explainer. The court noted — as guidance, not a finding of
   breach — that for high-risk scenarios touching personal or property safety,
   providers should escalate a mere *limitation* label to an actual *warning*
   label.
2. **Industry-standard technical measures to improve accuracy (行业通行技术措施).**
   Not a duty to exhaust or exceed the state of the art — a duty to deploy the
   accuracy measures generally adopted in the industry and to perform at least
   at market-average level. The provider showed hallucination
   detection/governance, supervised fine-tuning and reinforcement learning,
   external keyword/classifier guardrails, and retrieval-augmented generation
   (RAG) plus the web-search option — consistent with CAICT and international
   reports, and ranking at or above peers on the third-party **SuperCLUE**
   Chinese factual-hallucination benchmark. Two pointed observations: the duty
   **rises** for professional medical/legal/financial contexts, but this was a
   general assistant carrying no claim to authority over gaokao advice, so no
   heightened duty applied; and Liang had **not enabled web search**, which
   itself degraded accuracy — a choice not chargeable to the provider.

Critically, the court ran a **burden-shift**: once the provider made out a
*prima facie* case that it had taken industry-standard measures, the burden
moved to Liang to show those measures fell short or that a specific flaw
existed. He offered no such rebuttal evidence, and bore the adverse
consequence. So: no intent, no negligence → **no fault**.

## No damage, no causation

Two independent further grounds, each fatal:

- **No proven damage.** Liang's claimed loss — a forgone application
  opportunity, plus verification and litigation costs — is *pure economic
  loss*, with remote causation, and he submitted **no evidence** any of it
  actually occurred. No damage, no compensation.
- **No causation.** The inaccurate answer never substantively entered Liang's
  decision-making: he spotted and corrected it within the same short exchange
  (the eighteen minutes), so the test — *but for* the conduct the harm would
  not have occurred, and such conduct *ordinarily* produces such harm — is not
  met.

**Result: every claim dismissed.** Case-acceptance fee of ¥50 borne by Liang;
appealable to the Hangzhou Intermediate People's Court within fifteen days.

## The court's framing: development and security, and the "chilling effect"

The judgment closes on policy. The AI era is here; the law should weigh
development *and* security, innovation *and* rights protection in equal
measure. Loading strict liability onto a high-public-value technology in its
infancy risks a **"chilling effect" (寒蝉效应)** that keeps useful
applications from launching. A fault standard, by contrast, lets a court
appraise the provider's *whole* course of conduct — hard red lines against
toxic/illegal content, plus incentives to take reasonable safeguards — and
calibrate the duty of care dynamically as the technology and its uses evolve.

## What this means for overseas counsel

- **This is now the operating liability surface for GenAI in China.** Service,
  not product. Fault, not strict liability. Any company offering a generative-
  AI service to the Chinese public is judged on whether it discharged a
  *process* duty of care — not on whether an individual answer happened to be
  wrong. Build the program against that standard.

- **The duty of care is a documented checklist, and the provider won on
  evidence.** What carried the day was not argument but proof: a notarised
  record of the in-product disclaimers and labels, the user-agreement bold-
  face carve-outs, the model-principles explainer, third-party benchmark
  rankings (SuperCLUE), competitor-reproduction tests, and technical reports
  on hallucination governance. Assemble and *preserve* this evidence **before**
  a dispute — once you make out a *prima facie* case, the burden shifts to the
  claimant.

- **Get the labelling right to the letter.** Conspicuous, end-of-answer
  AI-generated-content labels conforming to the
  [AI content-labelling Measures](/laws/ai-content-labeling-measures/) (and
  GB 45438—2025, with the labelling chain running through the
  [Deep Synthesis Provisions](/laws/deep-synthesis-provisions/)) were a
  load-bearing part of the win. The first round here carried *no* label; in a
  closer case that gap could matter.

- **Escalate for high-stakes use.** The duty of care **rises** for medical,
  legal, and financial output, and the court signalled that high-risk
  scenarios touching personal or property safety call for a *warning* label,
  not merely a *limitation* label. If your model gives professional-domain
  answers or claims domain authority, plan for the heavier standard — generic
  disclaimers will not be enough.

- **An AI's words do not bind you — unless you let them.** The model's
  "promise" failed because the company never manifested an intent to be bound
  and had expressly disclaimed one. As AI agents begin to negotiate, commit,
  and "authorise," that disclaimer architecture in your user terms is what
  keeps generated text from becoming a corporate undertaking. Audit it.

- **Causation and damage remain real defences.** Even past fault, a claimant
  must prove actual loss and a causal link. The "eighteen minutes to self-
  correction" framing shows Chinese courts will look hard at whether the bad
  output genuinely drove any decision — but do not plan around it; the durable
  protection is the discharged duty of care, not the hope that the user
  noticed in time.

The deeper signal tracks what [Zhu Xiaofeng has argued](/posts/zhu-xiaofeng-genai-pi-causation-unclear-liability/)
about GenAI causation: Chinese AI tort law is being built out of Civil Code
architecture and a calibrated, evolving duty of care — not the product-
liability or strict-liability reflexes familiar from other jurisdictions.
Companies that build their China GenAI compliance against *this* frame —
process-duty documentation, exacting content labelling, escalation for
high-stakes domains, and a clean declaration-of-intent firewall in their terms
— will operate it efficiently. Those waiting for a statute will be retrofitting
under worse conditions.

---

*Source: Hangzhou Internet Court, *Liang v. a certain AI company*, (2025) Zhe
0192 Min Chu No. 18143 (杭州互联网法院（2025）浙0192民初18143号), judgment dated
3 December 2025; written into the Supreme People's Court's work report to the
NPC on 9 March 2026. Judgment text circulated via 北大法宝 / 知产库 and
reposted via the 教授加 WeChat Official Account. [Original repost (Chinese).](https://mp.weixin.qq.com/s/8XA_qZiuOcZYrEen1vwaWQ)*

*Not legal advice. The above is DCC's structured summary of, and commentary on,
the judgment, with framing for overseas counsel.*