arXiv:2607. 19292v1 Announce Type: cross Abstract: Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios.
By Gjergji Kasneci, Enkelejda Kasneci
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
arXiv:2607. 03215v1 Announce Type: cross Abstract: Artificial intelligence has spread across the whole of the security lifecycle.
By Mohamed Chahine Ghanem
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:2608. 05173v1 Announce Type: cross Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole.
By Peter Barnett
arXiv:2606. 17286v1 Announce Type: cross Abstract: AI-enabled authoritarianism is not confined to autocracies.
By Jeba Sania, Marta Ziosi, Fazl Barez