To support the “Qinglang — Rectifying AI Application Disorder” special action, the CAC Reporting Centre opened a dedicated reporting channel for “AI application disorder” from June 2026, receiving public reports. The scope mainly covers failures to implement generative-content labelling and the production and dissemination of false, violent or vulgar information.
What one reporting channel changes
The special action itself is not new — “Qinglang — Rectifying AI Application Disorder” was launched in April 2026, with phase one focusing on failures to perform large-model filing and registration duties, inadequate platform security and content-filtering capability, data poisoning, and incomplete content labelling.
What deserves attention is the “reporting channel”. It expands the source of enforcement leads from primarily regulatory inspection to a dual structure with public reporting as a complement.
The effect on companies is structural. Regulatory inspection, limited by manpower and coverage, can only sample; public reporting is decentralised and constant. A missing label complained about by a user, a piece of false content reported, can each become the starting point of individual enforcement.
What the reporting scope reveals
The scope explicitly lists two categories: failures to implement generative-content labelling, and the production and dissemination of false, violent or vulgar information.
These correspond to the two main lines of the special action. Content labelling is a technical, verifiable duty — whether a label is present and correct is visible to any user, making it the most easily triggered by reports. False and vulgar content corresponds to content-ecosystem governance, a continuation of earlier “Qinglang” campaigns.
In other words, the channel turns “violations perceptible to ordinary users” into a high-frequency enforcement entry point. For such issues, companies cannot rely on “regulators not finding it”.
Three levels of response
The first is self-inspection of the labelling chain. Generative-content labelling includes explicit labels visible in the interface and implicit labels in file metadata. What is most easily reported is often a missing or insufficiently visible explicit label. Companies should check both chains.
The second is building a reporting-response mechanism. Whether a report from a user or a platform referral can be verified, handled and recorded within a reasonable time directly affects whether remediation is found “timely”. Without such a mechanism, even a minor issue can lose points at the handling stage.
The third is feeding reporting data back into content governance. The types of issues concentrated in reports are direct input for optimising moderation strategy and product design. Treating reports as risk signals rather than mere nuisance is more effective.
Why “passive compliance” is increasingly uneconomic
The logic of the reporting channel is to connect social supervision to the regulatory chain. This means companies’ compliance performance is tested more frequently and more diffusely.
For foundational work such as content-moderation mechanisms, data-source ledgers and labelling implementation, doing it early costs less than doing it late. For support on content-moderation mechanisms, labelling implementation and reporting-response processes, see our Algorithm Filing and Data Compliance services.
This article is based on public information on the CAC Reporting Centre’s ‘AI application disorder’ reporting channel opened in June 2026. It is for reference only and does not constitute legal advice.