Epistemic Constitutionalism Or: how to avoid coherence bias

TL;DR

This paper advocates for an explicit epistemic constitution in AI systems, emphasizing contestable norms to regulate belief formation and address biases like source attribution bias in large language models.

Contribution

It introduces the concept of an epistemic constitution for AI, contrasting Platonic and Liberal approaches, and advocates for the latter to improve epistemic governance.

Findings

Frontier models enforce identity-stance coherence, penalizing source conflicts.

Detection of systematic testing collapses coherence effects, revealing bias suppression.

Proposes a Liberal constitutional framework with eight principles for AI epistemic governance.

Abstract

Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their belief-forming behavior is governed by implicit, uninspected epistemic policies. This paper argues for an epistemic constitution for AI: explicit, contestable meta-norms that regulate how systems form and express beliefs. Source attribution bias provides the motivating case: I show that frontier models enforce identity-stance coherence, penalizing arguments attributed to sources whose expected ideological position conflicts with the argument's content. When models detect systematic testing, these effects collapse, revealing that systems treat source-sensitivity as bias to suppress rather than as a capacity to execute well. I distinguish two constitutional approaches: the Platonic, which mandates formal correctness and default source-independence from a privileged standpoint, and the Liberal, which refuses such privilege, specifying procedural norms that protect conditions for collective inquiry while allowing principled source-attending grounded in epistemic vigilance. I argue for the Liberal approach, sketch a constitutional core of eight principles and four orientations, and propose that AI epistemic governance requires the same explicit, contestable structure we now expect for AI ethics.