What Happens After the Signal: The Missing Layer in AI-Supported Decision-Making

This paper defines a critical and previously unstructured domain within AI-supported justice environments: the execution-layer decision space. While existing governance frameworks focus on system design, validation, and outcome monitoring, they do not address how decisions are actually formed between the moment a system generates a signal and the moment institutional authority is exercised. This work introduces the concept of Decision Observability as a structured approach to making decision-making visible at the point where human judgment interacts with system outputs. It establishes the execution-layer decision space as a distinct and necessary layer of governance, with implications for accountability, defensibility, and operational improvement across corrections, courts, and supervision environments. This paper is part of the Justice Decision Observability™ (JDO™) Canon Series (JDO-2026), a structured body of work defining the field of execution-layer governance in AI-supported systems.