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?
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?
Institutional practice unknownOne PhD chapter or paperLow to medium costPartly 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
- China's CAC reported 1,112 generative-AI services filed and 731 applications/functions registered by 31 August 2026, up from 988 and 598 at the end of June.
Filing and registration counts rose from 988 and 598 to 1,112 and 731 between June and August 2026, but the claim explicitly says nothing about rejection rates, adoption or market share. - China's CAC notices require launched generative-AI applications or functions to display the filed/registered service or model they use, including the model name and filing or launch number.
Launched applications must display the filed service or model they use, with its name and filing number, which makes model dependence observable; the claim does not license any measured concentration, which is what this study would supply.
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
- Who searched and checked: an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Every check so far was done by AI; no person or domain expert has checked it yet.
- What was read: none of the 3 nearest works below is recorded as judged from an abstract only. Not every entry says how much of the work was read.
- Where the search could not reach: Screened by web search on the date shown; Chinese-language academic databases (CNKI, Wanfang) were not reachable, and the Chinese-language search returned only news pages, so Chinese scholarship on the register is likely under-covered. The ChinaTalk post carries no date on the page; 2024 is inferred from its reference to August 2024 figures. Absence of a hit is not proof that the work does not exist.
- Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.
Nearest work found
- Schaefer, "Seeking the next DeepSeek: What China's generative AI registration data can tell us about China's AI competitiveness," Trivium China (29 Apr 2025)
Compiles 3,739 records from the deep-synthesis and generative-AI registers and analyses them by firm, region, sector (labels assigned by an LLM and spot-checked) and state affiliation. Does not record base-model dependence or rejections; a starting dataset rather than the study proposed here. - Sheehan & Du, "What China's Algorithm Registry Reveals about AI Governance," Carnegie Endowment for International Peace (9 Dec 2022)
Infers what the earlier algorithm registry collects from its user manual and the first batch of 30 public filings (August 2022). Predates the generative-AI filing regime; no data on outcomes of filings. - Bit Wise (pseudonym), "SB 1047 with Socialist Characteristics: China's Algorithm Registry in the LLM Era," ChinaTalk (undated, 2024)
Practitioner account of how generative-AI filing works, describing repeated rounds of rework after rejection; states that no good data on failed filings exist because only successful filings are published. Qualitative, no counts.
Search queries used
China generative AI service filing registry analysis dataset CAC filed models empirical study algorithm registryChina 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
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.