AI 中文总结
本研究利用LLM处理英国上市公司年报，构建两阶段分类流程，揭示AI风险披露从2.8%升至41.2%但实质性披露仅4.3%，且行业差异显著，为衡量社会韧性提供新信号。
AI 中文摘要
社会韧性研究依赖于获取有用且可操作的数据，这引出了我们的主要研究问题：能否通过大规模处理年报，利用大语言模型（LLM）提供关于公司如何披露其对AI回应的有用信号？我们通过将可复现的两阶段分类流程应用于来自1,362家英国上市公司（2020-2025年，含部分2026年数据）的9,821份年报来测试这一点。我们首先针对474个人工标注的段落验证该方法，发现其具有高召回率和中等程度的标签级一致性。随后，我们报告了三个实证模式：（i）从2020年到2025年，提及AI风险的年报比例从2.8%上升至41.2%，而AI采用披露比例也从13.8%上升至45.2%，且具名供应商提及集中于以微软为首的一小部分主要提供商；（ii）披露情况因关键国家基础设施行业和市场细分而存在显著差异：AIM报告中的AI风险披露率远低于主板市场报告，且能源和数据基础设施等行业在AI风险披露方面落后于其他行业；（iii）危害披露几乎不存在（整个语料库中仅有七份报告）。我们开发了一种实质性分类来评估披露质量，发现大多数AI风险披露并不具有实质性：2025年，所有报告中41.2%将AI提及为风险，但仅有4.3%包含我们归类为实质性的AI风险披露。
英文摘要
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.
Comments22 pages (9 main text + appendices), 12 figures, 7 tables. Code and data: https://github.com/84rt/AI-Risk-Observatory (release dataset-v1.1)
