This work provides 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.
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.
Generative AI encodes the majority's way of knowing as the default infrastructure of knowledge itself as the default infrastructure of knowledge itself, and law must learn to govern at that level of model training.
This article develops a sociology of generative knowledge for the age of AI. It treats contemporary systems as hybrid actors within sociotechnical networks and reframes classical anchors – Mannheim’s situatedness, Merton’s norms, and Latour’s distributed agency – for model-mediated inquiry. Generative knowledge is defined as knowledge organized to produce further knowledge through iterative, tool-mediated, and socially embedded processes. On this basis, the paper advances epistemic stewardship as a practical orientation that sustains human agency while harnessing computational acceleration. Stewardship is operationalized through transparency-by-design, provenance and traceability, calibrated trust, independent verification and red teaming, structured challenge routines, inclusivity in data and participation, and proportional delegation to machines. The account clarifies gains in discovery and education alongside risks from opacity, automation bias, feedback loops, and cognitive drift, and it specifies institutional reforms: standardized disclosure artifacts, replication triggers for AI-assisted claims, revised authorship taxonomies, and equitable access to compute, benchmarks, and community-governed datasets. A research agenda follows, calling for comparative evaluations of stewardship designs, longitudinal studies of hybrid practice, and field-specific protocols that link evaluation to adoption thresholds. The result is a framework that integrates social theory with design and governance guidance, aiming to convert acceleration into certified advance while keeping responsibility legible and contestable.
Paolo Granata· Journal of Emerging Perspect...· 0 citations
We investigate the structure of interactive beliefs in networks: the epistemic state in which agents hold, revise, and act on their models of the epistemic states of other agents. What a group believes depends on what each member agent takes the others to believe, and on what each takes the others to believe about still others. We posit that the proper unit of social-epistemic analysis is not the individual belief but the tensor of mutual attribution, the array that records what every agent takes every agent to believe. We separate three layers of this object: what is privately held, what is publicly expressed, and what is to be believed by others. Social belief evolves by contraction of the tensor against a signed matrix of epistemic influence. Collective misperception decomposes exactly into a component that observation dissolves and a component that observation cannot touch. Social conformity amplifies pluralistic ignorance without generating it. The stable forms of collective epistemic consensus, polarization, entrenched misperception, and instability are spectral regimes of a single operator. Higher-order belief reduces to walks in the epistemic network, under a stated assumption of cognitive consistency whose boundary we mark precisely.
Large language models are increasingly deployed as autonomous agents serving users on behalf of companies, placing them in settings where user and deployer interests can conflict. When an agent knows that a user is owed something its deployer would prefer to deny, does it remain honest? Answering this is difficult because false statements can reflect either ignorance or hallucination rather than deception. To address this challenge, we introduce KnownLieBench , a knowledge-verified benchmark that first confirms through a neutral probe that an agent knows a user's entitlement, and then evaluates whether it makes false claims once an incentive to deny that entitlement is introduced. Specifically, KnownLieBench covers eight customer-service domains and 112 grounded cases, conducts multi-round dialogues with a trust-tracking customer agent, and separates deception emerging from incentive alone from deception produced under explicit instruction. Across eighteen proprietary and open-weight models, emergent deception varies substantially across model families and domains. We further use the benchmark for post-training, finding that honesty-directed fine-tuning reduces deception under incentive, while deception-graded fine-tuning increases lie success on honest-control dialogues without increasing lie frequency under incentive. By verifying entitlement knowledge before scoring deceptive behavior, KnownLieBench reduces the confound between lying and not knowing and enables more rigorous auditing and steering of agent honesty.
Zheyuan Liu, Weiliang Zhao, Xiangchi Yuan et al.· 0 citations
Building on interdisciplinary research that bridges the humanities, social sciences, and computational design, the book develops frameworks such as Algorithmic Epistemology Theory and Cognitive-Epistemic Modeling to explain how truth is co-produced by human and computational actors.
