Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
The paper outlines an Adversarial Social Epistemology (ASE) for understanding how trust is manipulated in communicative environments where assertions rely on chains of testimony, inference, and institutional certification. It argues that existing concepts like epistemic bubbles and echo chambers are inadequate, and proposes mechanisms for auditing and redressing trust breaches by focusing on the auditability of inferential chains within epistemic networks enriched with inferentialist semantics.
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[Submitted on 8 Jul 2026]
Title:Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
View a PDF of the paper titled Adversarial Social Epistemology for Assemblies of Humans and Large Language Models, by Mihnea C. Moldoveanu and Joel A.C. Baum
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Abstract:We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust. In such landscapes, agents have incentives and affordances to distort, color, omit, fabricate, or strategically under-specify information for private, reputational, rhetorical, or material gains. We argue that these phenomena are not adequately captured by familiar descriptions of epistemic bubbles, echo chambers, or misinformation diffusion. What requires explanation is how communicative agents exploit the commitments and entitlements that normally make scaffolded assertions trustworthy. We provide language that delivers the requisite analysis, outline mechanisms that subvert trust in scaffolded public communications, and outline machinery for auditing and redressing trust breaches arising from subverting the auditability of inferential chains, drawing on epistemic networks, enriched with an inferentialist semantics for interpreting assertions.
Comments: 50 pages
Subjects:
Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
Cite as: arXiv:2607.07760 [cs.AI]
(or arXiv:2607.07760v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.07760
arXiv-issued DOI via DataCite
Submission history
From: Mihnea Moldoveanu [view email] [v1] Wed, 8 Jul 2026 15:09:49 UTC (412 KB)
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