This paper defines AI Reliance Logging as a distinct evidentiary control class concerned with preserving AI-generated representations that may later become the object of reliance in governed decision environments. It explains the procedural failure mode that arises when AI outputs cannot be reconstructed at the moment scrutiny occurs, clarifies what AI Reliance Logging does and does not encompass, and situates the control within established audit, legal, and disclosure oversight doctrine. The paper is implementation-agnostic and does not prescribe a specific technical solution, focusing instead on the evidentiary obligation that emerges as AI systems increasingly mediate consequential decisions.
This paper defines AI Reliance Logging as a distinct evidentiary control class concerned with preserving AI-generated representations that may later become the object of reliance in governed decision environments. It explains the procedural failure mode that arises when AI outputs cannot be reconstructed at the moment scrutiny occurs, clarifies what AI Reliance Logging does and does not encompass, and situates the control within established audit, legal, and disclosure oversight doctrine. The paper is implementation-agnostic and does not prescribe a specific technical solution, focusing instead on the evidentiary obligation that emerges as AI systems increasingly mediate consequential decisions.