arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.
arXiv:2609.16213v1 Announce Type: new Abstract: Artificial intelligence is reshaping biological research across an increasingly connected digital-to-physical workflow. General-purpose large language...
arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.
arXiv:2608. 05656v1 Announce Type: cross Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.
arXiv:2605. 02050v2 Announce Type: replace-cross Abstract: This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies).
arXiv:2607. 25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources.
The Global Index on Responsible AI 2026 (GIRAI) 2nd Edition refines its predecessor by distinguishing between framework existence and implementation, expanding from three to five thematic areas, and adding granular variables for framework quality. It evaluates responsible AI governance across five dimensions—Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and Use of AI in Public Service—using 38 indicators organized into three pillars: AI Policy, CSO Engagement, and Enabling Conditions, plus a separate Use of Unacceptable Risk AI penalty. Data from 135 country-level researchers and secondary sources are normalized to a 100-point scale, weighted by pillar importance, and used to facilitate systematic cross‑national comparisons for policymakers, civil society, and AI developers.
arXiv:2608. 13272v1 Announce Type: new Abstract: A small number of firms based in two states produce the most capable frontier AI models.
arXiv:2608.29478v1 Announce Type: cross Abstract: Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intellig...
arXiv:2607. 12200v1 Announce Type: new Abstract: As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone.
The paper introduces LAAF, a Layered Accountability Architecture Framework for Large Language Model (LLM) applications, developed through a systematic review of 122 primary studies and 12 regulatory documents. It identifies five accountability dimensions and four families of mechanisms—technical controls, human oversight, organisational governance, and documentation/traceability—evaluated across maturity levels. The framework maps onto major standards such as the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sectoral guidance, highlighting gaps in human oversight, accountability metrics, disciplinary alignment, and empirical validation.
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.
arXiv:2512. 15783v3 Announce Type: replace-cross Abstract: This paper proposes a measurement standardisation framework that compresses expert-AI interactions into structured, comparable fields for prospective risk detection in deployed AI systems, without access to model internals.