The study examines whether annual reports can reveal how companies disclose AI-related risks and responses. Using a two-stage classification pipeline on 9,821 reports from 1,362 UK listed firms (2020‑2026), the authors find that mentions of AI risk rose from 2.8% to 41.2% and AI adoption disclosures from 13.8% to 45.2%, with most risk mentions clustering around major vendors like Microsoft. Disclosure varies by sector and market segment, with Critical National Infrastructure and AIM reports lagging, and substantive risk disclosures remain rare—only 4.3% in 2025. "Why It Matters": The findings show that while AI risk is increasingly referenced in corporate reports, substantive disclosures are scarce, highlighting a gap in transparency that could affect societal resilience.
arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.
arXiv:2606. 09809v1 Announce Type: new Abstract: AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs.
The paper examines AI disclosure policies in top computer science venues, finding them to be highly under‑specified. A survey of 109 researchers shows that disclosures are deemed most necessary for research design tasks and when human involvement is low, and it compiles researchers’ expectations for disclosure content. Analysis of 13,867 disclosure statements from EMNLP 2025 and ICLR 2026 reveals a significant mismatch between these expectations and actual practice, such as frequent disclosure of writing assistance despite it being considered less necessary.
The study evaluates 684 U.S. federal AI governance documents for how they address 14 sectors and 24 AI risks, measuring both breadth and depth of coverage. It finds that risks related to robustness, system security, and governance are more frequently and substantively discussed than socioeconomic, environmental, and emerging risks, and that sectors such as public administration, national security, information, and scientific services receive higher coverage than finance and healthcare. By comparing these coverage patterns with expert vulnerability assessments, the authors identify potential gaps in AI governance that could inform future policy and industry decisions.
The paper discusses the need to adapt incident reporting frameworks for AI agents, which are rapidly deployed and face unique security challenges. By comparing AI systems and agents and consulting 23 experts, the authors identify key reporting elements such as agent memory, autonomy levels, and tool usage. They also highlight open research questions, potential reporting weaknesses like data leakage, and outline privacy requirements for secure AI agent deployment.
The study shows that while large language model (LLM) annotations of stakeholder consultation submissions are highly reproducible (intraclass correlations > 0.99), they do not reliably capture the intended construct measured by structured survey responses. Divergence between LLM-inferred and survey measures varies by stakeholder group, with business associations expressing more AI risk concern in text than in surveys, and spatial autocorrelation indicates neighboring European countries share similar text-based stances. Despite these divergences, survey-reported concerns remain strongly linked to support for explainability across all levels of divergence.
As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policymakers to enact policies to disclose AI use at mul...
arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.
arXiv:2607. 24243v1 Announce Type: new Abstract: Mainstream AI research emphasises capability growth and tolerates low failure rates when average-case performance is high.
The paper introduces the Systemic Risk Index, an open pipeline and dashboard that aggregates evidence from 19 public AI benchmarks into four systemic‑risk categories defined by the EU GPAI Code of Practice. It evaluates 18 models using harm‑preserving perturbations and simulated deployment contexts, offering users the ability to switch between average and worst‑case aggregation and to trace each risk rating back to its benchmark evidence. The study finds that worst‑case scores can be 14 to 37 points lower than average scores, and that LLM judges agree with human graders at a level comparable to human‑human agreement.
Claims about AI safety reach audiences well beyond the AI community, yet many rely on opaque evidence or static assessments, when supporting evidence is accessible at all. We present the Systemic Risk...
arXiv:2511. 05613v2 Announce Type: replace-cross Abstract: Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities.