Lights-off Data Factory: Measuring Epistemic Autonomy in Governance-First Data Systems

Abstract

Autonomous data systems are increasingly claimed by vendors and practitioners, yet no shared metrics exist to distinguish true epistemic autonomy from automation that remains dependent on human arbitration. Prior work in data integration, governance, and AI ethics has highlighted the scalability limits of human-centric oversight models. This paper introduces a metric framework for governance-first autonomous data systems comprising three orthogonal measures: the Reflective Autonomy Quotient (RAQ), measuring semantic correctness under autonomous operation; the Resilience Entropy Quotient (REQ), measuring governance brittleness under uncertainty; and the Stewardship Singularity Threshold (SST), a phase-transition criterion separating human-dependent from autonomous governance regimes. Using a large-scale simulation of 100,000 entity-resolution decisions, we demonstrate that these metrics clearly distinguish legacy human-in-the-loop systems from Level-5 autonomous governance systems.