The Federal AI Use Case Inventory — How Many AI Systems US Agencies Actually Report Using
United States of America
Under OMB Memo M-24-10, US federal agencies (except DOD and intelligence) must inventory their AI use cases yearly and publish …
China · Beijing · See the China profile
Since March 2022, China's Cyberspace Administration has required platforms to file algorithm and AI disclosures before deployment — 190 generative-AI models filed by August 2024 — but independent review finds the public disclosures too vague for real oversight.
China's Provisions on the Administration of Algorithmic Recommendation for Internet Information Services, issued by the Cyberspace Administration of China (CAC) and effective 1 March 2022, require platforms whose algorithms have "public opinion properties or social mobilisation capacity" to file for a registration number with CAC or its provincial branches. A parallel regime under the 2023 Interim Measures for Generative AI Services extended filing obligations to generative-AI models and applications.
By August 2024, CAC had confirmed that 190 generative-AI models had filed successfully under the regime, and independent tracking around the same period found that only a portion of submitted models (estimated near half) cleared filing at any given time — evidence the mechanism screens submissions rather than acting as a rubber stamp, and that it now operates at national scale across thousands of platforms' recommendation algorithms as well.
However, an independent review by the Carnegie Endowment for International Peace of the disclosures made public found them "pitched at such a high level as to be almost completely devoid of meaningful detail": Weibo's "hot search" ranking algorithm, for example, is described only as combining "search popularity, discussion popularity, and dissemination popularity" multiplied by an "interaction rate coefficient" — a description Carnegie says an outside observer with no prior knowledge of the algorithm "could essentially guess." Carnegie concludes that, based on what is public, the filings "provide no meaningful insights into the algorithms, how they were trained, or how they might perform," even though non-public sections of the filings (training datasets, security self-assessments) may contain more detail that only regulators can see. Included here as an honest cautionary case: a compliance mechanism that has scaled institutionally and legally but delivers little of the public accountability that algorithm registers elsewhere aim to provide.
Read the full analysis: https://digichina.stanford.edu/work/translation-internet-information-service-algorithmic-recommendation-management-provisions-effective-march-1-2022/
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United States of America
Under OMB Memo M-24-10, US federal agencies (except DOD and intelligence) must inventory their AI use cases yearly and publish …
Ukraine
DOZORRO's ML risk-flagging tool screens Ukraine's Prozorro procurement tenders for corruption red flags, catching 26% more unfair-selection and 298% more …
Nigeria
B’Odogwu (Nigeria’s Unified Customs Management System) applies AI risk-profiling and ML pattern detection across 34 commands. Between Oct 2024 and …
Canada
Since April 2019, Canada's Directive on Automated Decision-Making has made federal departments score AI risk and publish Algorithmic Impact Assessments …
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