arXiv AI By Joshua A. Kroll, Andrew Smart, R. Stuart Geiger, Abigail Z. Jacobs

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

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arXiv:2607. 14353v1 Announce Type: cross Abstract: As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems.

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arXiv AI
Sep 15

AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems

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