a-methodology-for-auditable-trustworthiness-levels-in-ai-lifecycle-governance-a53ce5c7·1 events·first seen Aliases: A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
A new arXiv preprint proposes a two-component methodology for tracking AI trustworthiness across a system's lifecycle: a formal framework that learns interpretable trustworthiness levels from measurable dimensions using decision trees, and a governance procedure for design-time labeling, post-deployment monitoring, reassessment, and reporting. The approach introduces diagnostics such as boundary margins and profile drift to detect when a deployed system's trustworthiness has changed materially. The work targets the gap between high-level AI governance frameworks and the concrete, auditable documentation needed for regulatory conformity.