Uncertainty-Calibrated Decision Thresholds in Bayesian AI Systems: A Governance-Oriented Framework
Tareef Fadhil Raham
*
Arab Board of Health Specializations, Baghdad, Iraq.
*Author to whom correspondence should be addressed.
Abstract
While Bayesian inference provides a principled way to quantify uncertainty, many AI systems still rely on fixed thresholds applied to posterior summaries. This practice ignores the role of uncertainty dispersion, meaning that similar posterior estimates can lead to actions with very different levels of stability and risk—especially across varying contexts, subpopulations, or deployment environments.
This paper introduces Bayesian Epistemic Zoning (BEZ), an interpretive framework that evaluates operational decision thresholds relative to posterior dispersion. By standardizing decision values with respect to posterior uncertainty, the framework partitions decision thresholds into interpretable regions—Trusted, Upper Plausible, Lower Plausible, Inflated, and Recessive—reflecting differing levels of decision stability and evidential support. In addition, BEZ enables inverse derivation of admissible decision thresholds from posterior uncertainty constraints, replacing convention-based cutoffs with dispersion-calibrated decision limits.
Simulation experiments demonstrate that dispersion‑aware classification can reveal instability and overconfidence that remain hidden when relying solely on posterior means or credible intervals. In real‑world medical threshold analysis of malignancy risk, BEZ showed that clinically relevant cutoffs migrated across epistemic zones despite similar posterior point estimates, underscoring the practical importance of uncertainty‑aware evaluation. Specifically, thresholds that appeared stable under conventional point estimates were reclassified as fragile or inflated once posterior dispersion was accounted for, revealing hidden vulnerabilities in malignancy risk stratification. This demonstrates how BEZ can expose clinically significant shifts in credibility that conventional thresholding would overlook.
The proposed framework is model-agnostic, computationally lightweight, and compatible with standard Bayesian workflows. By linking posterior uncertainty directly to decision threshold evaluation, BEZ provides a practical mechanism for improving transparency, stability assessment, and uncertainty-aware oversight in automated and high-stakes decision systems.
Keywords: Bayesian inference, decision thresholds, uncertainty quantification, posterior dispersion, reliability-aware interpretation, probabilistic modeling, credibility zones