[Submitted on 1 Oct 2026]
Title:The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?
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Abstract:Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed at scale with LLMs, provide a useful signal about how companies disclose their response to AI? We test this by applying a reproducible two-stage classification pipeline to 9,821 annual reports from 1,362 UK listed companies (2020-2025, with partial 2026 data). We first validate the method against 474 human-annotated passages, finding high recall and moderate label-level agreement. We then report three empirical patterns: (i) between 2020 and 2025, the share of reports mentioning AI risk rose from 2.8% to 41.2%, while AI adoption disclosure also rose, from 13.8% to 45.2%, and named vendor mentions cluster around a small set of major providers led by Microsoft; (ii) disclosure varies substantially by Critical National Infrastructure sector and market segment: AIM reports disclose AI risk at far lower rates than Main Market reports, and sectors such as Energy and Data Infrastructure lag behind the rest in AI risk disclosure; and (iii) harm disclosures are near-absent (seven reports across the entire corpus). We develop a substantiveness classification to assess the quality of the disclosure and find that most AI risk disclosure is not substantive: in 2025, 41.2% of all reports mention AI as a risk, but only 4.3% contain AI risk disclosure we classify as substantive.
Comments: 22 pages (9 main text + appendices), 12 figures, 7 tables. Code and data: this https URL (release dataset-v1.1)
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.02281 [cs.AI]
(or arXiv:2610.02281v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.02281
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bart Jaworski [view email] [v1] Thu, 1 Oct 2026 12:03:05 UTC (405 KB)
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