When Are AI Explanations Recoverable? Identifiability, Stability, and Transfer from an Inverse-Problem Perspective
This paper establishes a mathematical framework for determining the recoverability of AI explanations by formulating them as inverse problems, deriving a complete trichotomy of identifiability and stability conditions for linear and nonlinear systems, and providing explicit bounds that distinguish intrinsic underdetermination from algorithmic variability.