Analyzing AI Output Reliability vs. Execution RisksAnalyzing AI Output Reliability vs. Execution Risks
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This work demonstrates how AI outputs can appear valid while still being unsafe to act on under real conditions.
It focuses on the gap between: – what a system produces – and what a system is actually allowed to execute
The analysis highlights: – how outputs can pass evaluation but fail at execution – why risk emerges at the transition from intent to action – where systems lack constraints at the point of commit
The goal is to identify where AI-driven decisions become actionable without being fully validated against current state, authority and conditions.

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