arXiv:2607.07760v1 Announce Type: new 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 misinform
The proliferation of sophisticated AI models, particularly Large Language Models, necessitates a new framework for understanding information distortion within complex human-AI communication networks.
A strategic reader needs to understand how AI influences the integrity of information flows and trust in public assertions, as this impacts decision-making, policy formulation, and societal stability.
Traditional epistemic models are insufficient for analyzing human-AI information landscapes, requiring new 'adversarial social epistemology' to account for systemic incentives for information manipulation.
- · AI ethicists
- · Cybersecurity firms specializing in AI forensics
- · Platforms implementing robust content provenance and validation
- · Uncritical information consumers
- · Organizations relying on naive trust in AI-generated information
- · Social media platforms without advanced content moderation
Increased research and development into AI-driven disinformation detection and mitigation techniques.
Development of new regulatory frameworks and institutional standards for information integrity in hybrid human-AI systems.
A potential 'epistemic arms race' where AI-powered deception tools are countered by equally sophisticated AI-powered detection and truth-assessment tools.
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Read at arXiv cs.AI