China · Beijing · Data and Automated Decision-Making

Beijing AI Content Detection Case

The platform hid a user’s contemporaneously written answer and imposed a one-day posting restriction based on algorithmic detection. Because the platform controlled the detector and its reasoning, it owed a reasonable, proportionate explanation on these facts; the unsupported action breached the contract.

Court or tribunal
Beijing Internet Court
Publication date
10 September 2025
Procedural status
First-instance judgment effective after withdrawal of the appeal; published as a representative case in September 2025

01

Factual background & dispute

  • The platform hid an answer and imposed a one-day posting restriction because it allegedly contained unlabelled AI-generated content.
  • The user denied using AI and the platform rejected the appeal.
  • The platform did not adequately explain the algorithmic basis for its conclusion.

02

Core issues & judicial focus

  1. Whether the platform could review and enforce against unlabeled generated content under its user agreement
  2. What evidentiary and explanatory duties apply when an algorithmic finding is disputed
  3. Whether account enforcement lacking a factual basis constituted breach of contract

03

Judicial finding & holding

  • The text was written by the user at the time, while the platform controlled the detector and its reasoning; the platform owed a case-specific duty to explain its action.
  • The platform failed to reasonably explain the detection basis and result, leaving its action unsupported and in breach of contract.
  • The court ordered the platform to restore the hidden answer and delete the associated internal violation record. The first-instance judgment became effective after the appeal was withdrawn.

04

Practical risk implications

01Generated-content detection requires documented thresholds, human review, and false-positive handling.

02Visibility restrictions and account sanctions should provide concrete reasons and appeal channels.

03Platforms should retain model versions, detection outputs, human review, and appeal records.

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