How Large Organisations Classify AI in Public Governance Documents
Legacy recordInquiry working paper
Published 26th June 2026
A documentary study of how fifteen large organisations classify AI in their public governance documents.
Reading guide
The archive is built as a connected record rather than a stream. This map groups the published record by the question each record addresses.
Section 1
Records in this group ask how AI is named, governed, and made legible to the institution responsible for it.
Inquiry working paper
Published 26th June 2026
A documentary study of how fifteen large organisations classify AI in their public governance documents.
Working paper
Published July 2026
A reading of nine public frameworks that guide boards and audit leaders on AI oversight. None require anyone to know about the organisation-authored instruction that controls what the AI produces: who wrote it, when it was last changed, or how it affects answers. The phrase "system prompt" does not appear in any document reviewed.
Working paper
Published July 2026
A public-record study of how large organisations require, incentivise, and formalise AI engagement through mandates, incentives, training, workflow integration, and board-level expectation.
Section 2
These studies examine the governed layer itself: what it removes, what it preserves, and whether anyone can step around it at the moment of use.
Inquiry working paper
Published June 2026
A controlled test of whether the governance wrapping that makes a model deployable inside an institution removes specific forms of capability while preserving the appearance of intelligence.
Working paper
Published July 2026
A documentary reading of seven enterprise AI deployments showing that public administration and compliance documentation does not recognise executive rank as a query-time basis for varying the governed layer.
Testing method
Methodology locked in, 6 July 2026
The committed prompt set and scoring criteria for a comparative study of how different AI setups respond to strategic decisions from senior leaders. Five scenarios run through three setups each, producing fifteen answers scored blind on whether they take a clear position, add fresh insight, and serve as material a senior leader could debate. The methodology was locked before any outputs were generated.
Section 3
This thread reads public corporate language to see what organisations say about AI, what they avoid saying, and how that prose changes over time.
Inquiry working paper
Revised working paper, June 2026
A coding study of how twenty-two major firms publicly describe their AI programmes in prepared management remarks.
Inquiry working paper
Published March 2026
A systematic review of narrative disclosure prose across large US public companies, comparing 2019, 2022, and 2024 filings.
Working paper
Published July 2026
A review of the public record revealing the significant corporate disclosure gap regarding exactly how senior executives at large organisations access AI capabilities.
Section 4
These records test how accountability is written into law, contractual terms, and practical oversight arrangements.
Examination
Published June 2026
An examination of Article 14 and Article 26 of the EU AI Act against the psychological evidence on automation bias.
Working paper
Published July 2026
A reading of the public terms for seven enterprise AI services. Across all seven vendors, the contracts make the customer responsible for AI output. The same contracts describe controls that affect that output: filters, system instructions, safety features, retrieval rules, data masking, and classifiers. The clauses that assign responsibility do not mention those controls.