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:2607. 12588v1 Announce Type: new Abstract: According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act.
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
arXiv:2608. 11251v1 Announce Type: cross Abstract: Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act.
arXiv:2507. 11548v3 Announce Type: replace-cross Abstract: The use of publicly available generative AI systems for resume evaluation is often justified by the assumption that these tools reduce bias relative to human judgment.
arXiv:2601. 09753v2 Announce Type: replace-cross Abstract: AI science evaluation tools aim to assess research credibility.
Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness.
arXiv:2609.14796v1 Announce Type: new Abstract: The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion a...
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: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.
arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
arXiv:2608. 05656v1 Announce Type: cross Abstract: Safety risks of AI are becoming increasingly evident in human interactions with AI technologies.