The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?

arXiv:2610.02281v1 Announce Type: new 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.

Study finds increased AI risk disclosure, but most is not substantive.

The study documents a measurable gap between nominal AI risk disclosure and substantive, actionable reporting, with harms almost never disclosed. This suggests weak societal visibility into AI risks within critical sectors, a modest but real governance concern rather than an acute harm.

This academic paper analyzes company disclosures about AI risk and adoption in annual reports. It reports empirical patterns without making claims about AI's direct societal impact. The findings concern disclosure rates and quality, not the actual risks or benefits of AI itself.

The study shows increasing mention of AI risks and adoption in annual reports. However, substantive disclosure remains low. The implications for societal resilience are uncertain.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   fc56fd5a-6c94-4ee3-9356-11867b83bcf1
           https://arxiv.org/abs/2610.02281
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EVIDENCE   3 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -50  (Adverse)
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CONFIDENCE 75%