SOURCE-LINKED INTELLIGENCE
AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems
Artificial intelligence systems are rapidly becoming critical components in healthcare, finance, public services, and other safety-critical domains. Yet the engineering practices used to evaluate these systems remain predominantly model-centric, emphasizing properties such as accuracy, robustness, fairness, and interpretability before deployment. These properties are necessary but insufficient once an AI system operates within an ever changing socio-technical environment characterized by distribution shifts, institutional constraints, human feedback loops, privacy requirements, and interaction
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-13T15:18:41.000Z
First collected: 2026-09-20T12:21:05.240Z. This is not the publication date.