The paper systematically classifies the EU AI Act’s high‑risk requirements, finding that only a minority directly address AI‑specific risk sources while most impose organizational and documentation obligations. From these risk‑related requirements, the authors derive a consolidated list of distinct AI‑specific risk sources, creating an EU AI Act Risk Source List. This list aims to bridge the gap between legal obligations and AI risk‑management practice by providing a structured reference for comparing the Act’s implicit risk coverage with existing AI risk taxonomies.
By Ronald Schnitzer, Mike Auer, Rumpa Choudhury, Andreas Hapfelmeier, Maximilian Hoeving, Isabelle Painter, Josiane Xavier Parreira, Sonja Zillner
arXiv:2408. 02379v2 Announce Type: replace-cross Abstract: Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act.
By Benjamin Fresz, Vincent Philipp G\"obels, Safa Omri, Danilo Brajovic, Andreas Aichele, Janika Kutz, Jens Neuh\"uttler, Marco F. Huber
arXiv:2502. 16184v3 Announce Type: replace Abstract: The EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems.
By Henrik Nolte, Miriam Rateike, Mich\`ele Finck
The article proposes a structured framework of behavioral indicators that could signal a progression toward potentially catastrophic threats from AI systems. Drawing on established methods from cybersecurity and national security, it defines clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior. The framework is intended to enable researchers and policymakers to implement evidence‑based monitoring protocols for rogue AI progression.
By T. Bauer, W. P. Kegelmeyer, E. Begoli, A. Sadovnik, T. Emerson, C. Corley, N. Generous, J. Moore, B. Bartoldson, R. Goldhan, M. Goldman, M. Greaves, M. J. D. Vermeer, B. MacLennan, D. Schulker, N. VanHoudnos, J. Bansemer, Y. Bengio
arXiv:2607. 09586v1 Announce Type: new Abstract: The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them.
By Hannah M. Liu, Rhea Saxena, Shiv Asthana
arXiv:2607. 16112v1 Announce Type: new Abstract: Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies.
By Wilber Sean Anterola, Matthew Ball, Luis F. Lafuerza, Markov Grey
arXiv:2508. 09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies.
By Wilder Baldwin, Sepideh Ghanavati, Manuel Woersdoerfer
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...
By Joshua Levy, Mick Yang, Kellin Pelrine
arXiv:2609.05749v1 Announce Type: new
Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,...
By Emilio Barkett, Alexander Kimpton, Daniel Graham, Yusuf Kundgol
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
By Murat Kantarcioglu
arXiv:2607. 23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
By Muhammad Tukur, Hayatullahi B. Adeyemo, Tao Chen, Nour Ali, Anis Zarrad, Rick Kazman, Marco Agus, Rami Bahsoon