Justice Decision Observability™ (JDO™) defines the execution-layer governance discipline responsible for documenting how human decision-makers interpret and act on AI-supported system outputs in real-world operational environments. This document, JDO-2026-008, establishes the Governance Event Reconstruction Protocol, a structured methodological standard for reconstructing decision pathways following critical incidents or governance-significant events within justice systems. The protocol provides a formal process for analyzing the sequence of interactions between automated signals, human interpretation, discretionary authority, and resulting operational outcomes. It enables the systematic reconstruction of decision conditions at the moment of authority, where human actors engage with system-generated information under real-world pressures. The Governance Event Reconstruction Protocol operationalizes the core principles of Justice Decision Observability™ by: Structuring post-event reconstruction of decision pathways Making human reliance behavior and discretion visible within institutional workflows Identifying how signals were interpreted, deferred, escalated, or acted upon Preserving a defensible record of decision-making conditions without evaluating system performance or assigning fault This protocol is system-level and non-adversarial. It does not assess algorithmic validity, legal compliance, or individual culpability. Instead, it documents the governance conditions under which decisions occurred, providing critical visibility into the human accountability layer that exists above execution infrastructure. JDO-2026-008 functions as a core operational component of the Justice Decision Observability™ canonical series, translating the field’s conceptual architecture into a repeatable analytical process that can be applied across corrections, community supervision, courts, and other AI-supported justice environments. Together with the broader JDO™ framework series, this document contributes to establishing a standardized approach for execution-layer governance, enabling institutions to understand, document, and govern decision-making behavior in high-stakes, AI-supported contexts.
Fleming et al. (Tue,) studied this question.
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