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Research opportunity · Regulation-in-Practice: Rights, Compliance & Developmental Steering

China's AI filing register: how often are filings sent back, and whose models do apps use?

In short: has this been done?

Related work: Partly addressed. Related studies cover parts of this question; what they leave open is set out below. Only a first-pass search has been done, so it is not yet known whether a fuller search would find a study that does the whole design.

How far checked: First-pass screen only. A first-pass AI web search on 12 October 2026. No deeper check yet. Every search and check so far was done by AI; no person or domain expert has checked them yet.

The searches, the nearest work found and what the search could not reach

Earlier version (v1)

This is RO-LD12-04 as it was published at v1, kept unchanged. The current version is v2; its changes are listed there.

Question

Using the successive public lists of filed generative-AI services and registered applications published by China's internet regulator, together with interviews with applicants and filing consultants, how often do filings go through rejection and rework before approval, how often do listed services later disappear, and how concentrated is the dependence of registered applications on a small number of filed base models?

Gap type: Institutional practice unknownOne PhD chapter or paperLow to medium costRelated work: Partly addressed

Why it matters

The filing count is rising quickly, but counts of successful filings say nothing about how hard the gate is or what the regime does to market structure. Rejection and rework rates would show whether filing works as registration or as de facto licensing; the declared-model information that registered apps must display would show whether the regime is channelling the application market onto a few approved models.

The debate lists this among what we still do not know: Chinese rejection or sanction rates under the filing regime

Evidence that motivates it

Each claim is shown as it read when this version was published.

A possible design

Two strands. (1) Register panel: download every published batch of the regulator's filing and registration lists (and provincial lists) since 2023, link entries across batches to build a panel, and record for each registered application the base model it declares (from the published list if given, otherwise from the in-app display the rules require, checked for a random sample). Measure concentration of applications on base models (shares and a standard concentration index) by sector and over time, and record services that drop out of later lists or appear in published enforcement-campaign notices. (2) Process evidence: 20–30 semi-structured interviews with applicant firms, compliance consultants and lawyers, recruited through industry networks, on the number of submission rounds, reasons for return, and time to approval; triangulate with any published regulator statements. Report rework as ranges from interviews, clearly labelled as such, not as an official rejection rate.

Data needed

Public CAC and provincial filing announcements; app-store pages and in-app displays for a sample; interview access in China (Mandarin required); ethics approval with attention to interviewee risk.

Feasibility

One PhD chapter or paper. Low to medium: researcher time, Mandarin-speaking assistance, travel for interviews or remote interviews. The regulator publishes only successful filings, so the register strand alone cannot give a rejection rate; the interview strand is essential and will give estimates, not counts. Interviewee confidentiality is a real constraint.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026; not yet checked in depth).

Analysts have compiled the public registration lists and broken them down by firm, region, sector and ownership (Trivium China, April 2025), and earlier work described the registry's design from its user manual (Carnegie, 2022) and its de facto licensing character from practitioner accounts (ChinaTalk). None reports rejection or rework rates, and none measures which base models registered applications depend on; Trivium only speculates that many large-model registrations are lightly fine-tuned copies of a few foundation models.

How strong this evidence is

Nearest work found

Search queries used
  • China generative AI service filing registry analysis dataset CAC filed models empirical study algorithm registry
  • China algorithm registry empirical analysis filings Carnegie Sheehan "algorithm registry" study entries
  • 生成式人工智能服务 备案 实证分析 备案清单 统计 研究 论文
Cite this page

SocioLens project. (2026, October 12). China's AI filing register: how often are filings sent back, and whose models do apps use? [Research opportunity RO-LD12-04, version v1]. SocioLens. https://sociolens-web.pages.dev/opportunities/ro-ld12-04/v1/

@misc{sociolens_ro_ld12_04_v1,
  author = {{SocioLens project}},
  title = {China's AI filing register: how often are filings sent back, and whose models do apps use?},
  year = {2026},
  date = {2026-10-12},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld12-04/v1/}},
  note = {Research opportunity RO-LD12-04, version v1}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Provenance

RO-LD12-04 v1. Drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-12 debate on 2026-10-12. Checked by an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Not yet reviewed by a domain expert. This is a research hypothesis, not a finding.

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