Justice systems increasingly rely on automated outputs including alerts, classifications, risk scores, and monitoring signals to inform operational decision-making across corrections, courts, and community supervision environments. Despite this reliance, no formal, standardized methodology has existed to measure how those outputs are interpreted, acted upon, or governed within real-world decision environments. The Justice Beacon Doctrine Framework™ (JB-DOF™) establishes the first structured operational assessment and scoring standard for execution-layer decision governance within AI-supported justice systems. Grounded in Justice Decision Observability™ (JDO™), the framework defines how decision-making processes can be systematically observed, documented, measured, and evaluated independent of system performance. JB-DOF™ operationalizes governance through five integrated pillars: Signal Integrity, Reliance Behavior Mapping, Discretion Governance, Institutional Pressure Mapping, and Outcome Integrity Monitoring. Version 1.3 introduces a formalized scoring model, cross-environment comparability, and integration with the Decision Risk Pattern Index™ (DRPI™) for pattern detection and risk identification. Rather than evaluating system accuracy, JB-DOF™ focuses on the execution layer where decisions actually occur establishing a defensible, observable, and scalable standard for decision governance in AI-supported justice systems.
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Stephanie Fleming
Janna M. Broaddus
United States Department of Justice
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Fleming et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69e3215140886becb65408f7 — DOI: https://doi.org/10.5281/zenodo.19613873