Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains

The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI systems. High-stakes domains demand both maximal predictive accuracy and transparent decision rationale. This article presents a five-dimensional analysis of this $142 billion annual gap, examines why post-hoc explainability fails, and outlines a resolution framework treating explainability as an architectural constraint.