🤖 AI Summary
This study addresses the challenge of tracing causes and processes when automated decision systems fail, a task inadequately handled by existing compliance governance mechanisms. The authors propose an operational governance evidence framework that integrates structural accountability diagnostics, decision trajectory tracing, sufficiency metrics for evidentiary support, and label-free monitoring. The framework’s applicability is validated across four canonical system architectures. The research uncovers a “governance coverage gradient” phenomenon and introduces an uncertainty cascade model to identify three types of structural discontinuities in agent-based AI systems, along with methods for their analytical extension. By formalizing four propositions that delineate the framework’s boundaries, the work demonstrates full fillability in rule-based engines while simultaneously revealing inherent structural governance gaps in agent-centric AI systems.
📝 Abstract
When automated decision systems fail, organizations frequently discover that formally compliant governance infrastructure cannot reconstruct what happened or why. This paper synthesizes an operational governance evidence framework -- structural accountability collapse diagnostics, decision trace schemas, evidence sufficiency measurement, and label-free monitoring -- into an integrated chain and analytically assesses its transferability across four decision system architectures. The cross-architecture comparison reveals a governance coverage gradient: deterministic rule engines achieve full DES-property fillability, hybrid ML+rules systems achieve partial fillability, classical ML systems achieve only minimal fillability, and agentic AI systems encounter structural breaks. We introduce the cascade of uncertainty, showing how governance failures propagate through serial dependencies between framework layers. For agentic systems, we identify three structural breaks -- decision diffusion, evidence fragmentation, and responsibility ambiguity -- and propose corresponding analytical extensions. Four propositions formalize the gradient, cascade compounding, delegation-depth effects, and extension sufficiency, establishing boundary conditions for the framework's valid operating envelope.