Evidence-as-Code: Making AI Governance Verifiable

Abstract

As AI systems take actions with consequences beyond a conversation, governance claims require closer examination. A policy may describe intended behavior, and an operational record may describe an event, yet neither alone establishes that a particular claim is justified. This article discusses Evidence-as-Code as an evidence-oriented perspective on AI governance: relevant information should be sufficiently clear, assessable, and appropriately structured to support critical review. The emphasis is on the meaning and limits of evidence, rather than a technical design. Drawing on established work in AI risk management, control assessment, provenance, and observability, the article examines accountability, evidence quality, uncertainty, and privacy. Its contribution is an educational synthesis within the SGAEIA research series. It does not introduce a validated protocol, establish conformity, or report empirical proof of the SGAEIA framework.

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