aiqbench analysis
This paper proposes a "safety-by-design" architecture that integrates model-level supervision with system-level controls to address multi-layered AI safety failures. By combining independent verification and evidence infrastructure with robust governance, the framework aims to ensure accountability and interoperability in high-stakes AI deployments. The work underscores that rigorous safety assurance is an integral component of AI development rather than an optional add-on.
AI Safety: Not Optional, Not Later
arXiv:2609.10630v1 Announce Type: cross Abstract: Incidents show that AI safety failures often arise across multiple layers. We present a safety-by-design assurance architecture combining model-level supervision, such as Scientist AI, with system-level controls over scaffolds and harnesses, independent verification, monitoring, and evidence infrastructure, supported by governance for accountability and evidence interoperability.
Abstract:Incidents show that AI safety failures often arise across multiple layers. We present a safety-by-design assurance architecture combining model-level supervision, such as Scientist AI, with system-level controls over scaffolds and harnesses, independent verification, monitoring, and evidence infrastructure, supported by governance for accountability and evidence interoperability.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2609.10630 [cs.SE]
(or arXiv:2609.10630v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2609.10630 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 140 words
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