The Truth Convergence Problem in Bayesian Artificial Intelligence: A Conditional Epistemological Analysis

This paper systematically examines the epistemological pursuit of truth in Bayesian artificial intelligence (AI), arguing that truth convergence is a conditional outcome rather than an inherent property of the algorithm. Bayesian reasoning guarantees formal convergence mathematically, but its alignment with reality depends on prior openness, evidence authenticity, independence, information richness, feedback mechanisms, and institutional support. The study analyzes the limitations of Bayesian convergence theorems, the learnability in open Bayesianism, AI failure modes in modern information ecologies, and design principles and ethical constraints. It emphasizes that truth convergence is not merely an algorithmic property, but a civilizational, collective, and ecological achievement. By implementing non-dogmatic priors, independent evidence channels, explicit modeling of evidence correlations, feedback loop corrections, and institutionalized epistemic humility, AI can pursue truth conditionally. Ultimately, Bayesian AI is both a tool for formal reasoning and a litmus test of civilizational epistemic capacity, providing actionable guidance for modern scientific rationality, ethical practice, and societal institutions.