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This paper proposes an "epistemic constitution" to regulate how large language models evaluate arguments and express confidence, specifically addressing biases introduced by source attribution. The author argues against a default of strict source independence in favor of a liberal framework that allows for principled consideration of a source's position when relevant to testimonial contexts. By defining explicit meta-norms for belief formation, the work directly tackles coherence bias and establishes a governance structure for AI reasoning systems.
Epistemic Constitutionalism Or: how to avoid coherence bias
arXiv:2601.14295v5 Announce Type: replace-cross Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their responses can leave the epistemic policies governing these evaluations implicit. This paper argues for an epistemic constitution for AI: explicit, contestable meta-norms regulating how systems form and express beliefs. Source attribution provides the motivating case. An exploratory audit suggested that expectations about a source's position intrude on argument evaluation. A preregistered study (arXiv:2609.35286) then found content-dependent effects of source attribution, with selected written evaluations supporting source-position fit as an explanation. The audit also revealed conflicting justifications for attending to sources. Source independence, however, is not a neutral default: in testimonial contexts, a source's position and the costs of speaking against interest can provide relevant evidence. I distinguish two approaches to epistemic constitution design: the Platonic, which mandates formal correctness and default source-independence from a privileged standpoint, and the Liberal, which rejects such privilege and protects conditions for collective inquiry while allowing principled source-attending grounded in epistemic vigilance. I defend the Liberal approach, sketch a constitutional core of eight principles and four orientations, and argue that AI epistemic governance requires explicit, contestable norms for evaluating testimony, responding to evidence, and revising judgements.
Abstract:Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their responses can leave the epistemic policies governing these evaluations implicit. This paper argues for an epistemic constitution for AI: explicit, contestable meta-norms regulating how systems form and express beliefs. Source attribution provides the motivating case. An exploratory audit suggested that expectations about a source's position intrude on argument evaluation. A preregistered study (arXiv:2609.35286) then found content-dependent effects of source attribution, with selected written evaluations supporting source-position fit as an explanation. The audit also revealed conflicting justifications for attending to sources. Source independence, however, is not a neutral default: in testimonial contexts, a source's position and the costs of speaking against interest can provide relevant evidence. I distinguish two approaches to epistemic constitution design: the Platonic, which mandates formal correctness and default source-independence from a privileged standpoint, and the Liberal, which rejects such privilege and protects conditions for collective inquiry while allowing principled source-attending grounded in epistemic vigilance. I defend the Liberal approach, sketch a constitutional core of eight principles and four orientations, and argue that AI epistemic governance requires explicit, contestable norms for evaluating testimony, responding to evidence, and revising judgements.
33 pages, 1 table. Substantial revision: empirical discussion updated in light of arXiv:2609.35286; exploratory-audit claims corrected against public logs. Appendix A: full per-log register. Appendix B: corrections to v4 and AI-assisted writing documentation. Philosophical argument clarified
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
arXiv:2601.14295 [cs.AI]
(or arXiv:2601.14295v5 [cs.AI] for this version)
From: Michele Loi Dr. [view email] [v1] Fri, 16 Jan 2026 07:36:30 UTC (4,210 KB) [v2] Tue, 27 Jan 2026 19:15:46 UTC (4,216 KB) [v3] Wed, 22 Apr 2026 11:33:20 UTC (549 KB) [v4] Thu, 11 Jun 2026 09:22:30 UTC (773 KB) [v5] Tue, 6 Oct 2026 23:13:18 UTC (38 KB)
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