Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
Quick Answer
The paper introduces Adversarial Social Epistemology (ASE) to analyze how agents manipulate trust in public communications, highlighting mechanisms that undermine the reliability of testimony and inference.
Quick Take
It critiques existing frameworks like epistemic bubbles and misinformation diffusion, proposing a new language for understanding trust breaches and auditing inferential chains in densely interactive environments involving humans and .
Key Points
- ASE addresses manipulation of trust in public assertions and testimony.
- Critiques existing models like epistemic bubbles and misinformation diffusion.
- Proposes mechanisms for auditing and redressing trust breaches.
- Focuses on densely interactive communicative landscapes involving AI.
- Introduces inferentialist semantics for interpreting assertions.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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