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[CS.AI] Adversarial Social Epistemology: A New Perspective on Human-LLM Interaction

Published at: 2026-07-10 22:00 Last updated: 2026-07-13 08:32
#AI #LLM #Social Epistemology

This paper introduces an Adversarial Social Epistemology (ASE) for densely interactive communicative environments, where public assertions are supported by chains of testimony, inference, institutional certification, and tacit trust. In such environments, agents are incentivized and afforded the means 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 typically make scaffolded assertions trustworthy.

We provide language for the requisite analysis, outline mechanisms that undermine trust in scaffolded public communications, and propose 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.

Blogger's Review: This paper delves into the manipulation and erosion of trust in complex social environments, offering a fresh perspective on the interactions between humans and large language models. Its proposed adversarial social epistemology provides an important framework for understanding the dynamics of information dissemination and trust, making it a noteworthy contribution to the field.

Original Source: https://arxiv.org/abs/2607.07760

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