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

AI 风险观察站：我们能从年度报告中的 AI 披露信息中学到什么关于社会韧性的知识？

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).

摘要： 社会韧性研究依赖于获取有用且可操作的数据，这激发了我们的主要研究问题：通过大语言模型（LLM）大规模处理后的年度报告，能否为企业如何披露其对人工智能的应对措施提供有用的信号？我们通过对 1,362 家英国上市公司（2020-2025 年，包含 2026 年部分数据）的 9,821 份年度报告应用可重复的两阶段分类流程，对这一问题进行了验证。

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;

我们首先针对 474 段人工标注的文本对该方法进行了验证，结果显示其具有高召回率和中等的标签一致性。随后，我们报告了三个实证模式：（i）在 2020 年至 2025 年间，提及 AI 风险的报告比例从 2.8% 上升至 41.2%，同时 AI 应用披露比例也从 13.8% 上升至 45.2%，且提及的供应商主要集中在以微软为首的少数几家大型提供商；

(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).

(ii) 披露情况因关键国家基础设施部门和市场板块的不同而存在显著差异：AIM（创业板）报告披露 AI 风险的比例远低于主板市场报告，且能源和数据基础设施等行业在 AI 风险披露方面滞后于其他行业；以及 (iii) 关于危害的披露几乎不存在（在整个语料库中仅有七份报告提及）。

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.

我们开发了一种实质性分类方法来评估披露质量，发现大多数 AI 风险披露并不具备实质性：2025 年，虽然 41.2% 的报告将 AI 列为风险，但仅有 4.3% 的报告包含了我们归类为“实质性”的 AI 风险披露。