The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge (2512.19570v1)

Abstract: We examine epistemological threats posed by human and LLM interaction. We develop collective epistemology as a theory of epistemic warrant distributed across human collectives, using bounded rationality and dual process theory as background. We distinguish internalist justification, defined as reflective understanding of why a proposition is true, from externalist justification, defined as reliable transmission of truths. Both are necessary for collective rationality, but only internalist justification produces reflective knowledge. We specify reflective knowledge as follows: agents understand the evaluative basis of a claim, when that basis is unavailable agents consistently assess the reliability of truth sources, and agents have a duty to apply these standards within their domains of competence. We argue that LLMs approximate externalist reliabilism because they can reliably transmit information whose justificatory basis is established elsewhere, but they do not themselves possess reflective justification. Widespread outsourcing of reflective work to reliable LLM outputs can weaken reflective standards of justification, disincentivize comprehension, and reduce agents' capacity to meet professional and civic epistemic duties. To mitigate these risks, we propose a three tier norm program that includes an epistemic interaction model for individual use, institutional and organizational frameworks that seed and enforce norms for epistemically optimal outcomes, and deontic constraints at organizational and or legislative levels that instantiate discursive norms and curb epistemic vices.

• The paper demonstrates that internalist justification is essential for knowledge, while LLMs offer only externalist, reliability-based outputs.
• It employs a framework combining virtue epistemology and dual-process theory to assess how individual cognitive outsourcing undermines collective rationality.
• The analysis warns that delegating reflective reasoning to LLMs threatens institutional knowledge, calling for targeted regulatory and normative safeguards.

Epistemological Ramifications of LLMs on Collective Intelligence and Institutional Knowledge

Synthesis of Internalist and Externalist Epistemic Justification

The paper systematically interrogates how LLMs recalibrate foundational concepts in epistemology with explicit attention to threats at both the individual and institutional scales. The central theoretical contribution is the articulation of a framework termed collective epistemology, predicated on the integration of internalist (reflective) and externalist (reliabilist) justification. The paper distinguishes internalist justification—the agent’s reflective, reason-based understanding of their beliefs—from externalist justification, which is rooted in reliable transmission mechanisms regardless of agent access to underlying reasons.

The author asserts that only internalist justification qualifies as a sufficient condition for knowledge, as it alone enables rational agents to apprehend the justificatory basis of their beliefs. Externalist justification, while necessary, is posited as insufficient: it acts merely as a mechanism for trustworthy information transmission. The synthesized framework establishes three necessary and sufficient conditions for what the paper terms reflective knowledge: (a) agents must understand the basis for evaluating propositions, (b) agents must assess the reliability of truth sources in the absence of this basis, and (c) agents are normatively obligated to apply these rational standards within their domain of competence.

Crucially, the paper affirms that rationality is fundamentally bounded and distributed. Bounded rationality and dual-process theory are assumed as baseline, driving the delegation of rational tasks across a collective—a condition for scalable collective rationality and, by extension, collective institutional knowledge.

The Limits of LLMs as Epistemic Agents

Drawing on distinctions from virtue epistemology and Sosa’s animal/reflective knowledge taxonomy, the work identifies LLMs as instantiations of externalist, reliabilist justification. LLMs reproduce reliable outputs by transmitting patterns and regularities encoded from training data, but critically lack the capacity for reflective access to their knowledge base. They are described as epistemically transparent only in so far as they transmit what has already been reasoned reflectively by human agents; they do not themselves possess or generate reflective justification.

LLMs are, therefore, categorized as mere reliable transmitters rather than knowers in the full epistemic sense. This constraint is theoretically substantiated by the black-boxing of both their learning process and outputs (due to opacity in parameters, training data, and representational structure) and persistently observed phenomena such as hallucination. These features are treated as structural, not incidental, and thus intrinsic to present-day LLMs regardless of improvements in alignment or RAG-style memory augmentation.

The institutional implication is profound: large-scale outsourcing of reflective epistemic tasks to LLMs risks significant atrophy in the reflective standards that enable knowledge growth and error correction within human collectives. This risk is exacerbated by the opaque, proprietary nature of leading LLMs and trends in information retrieval and seeking that favor minimum-effort, maximum-expediency pathways, as formalized by Zipf’s law.

Consequences for Collective Rationality and Epistemic Health

The adoption of LLMs within human workflows, institutions, and knowledge-intensive organizations is shown to pose distinctive epistemological threats. The author defines Local Epistemic Threats (LETs) as the individual-level outsourcing of learning and reasoning, resulting in skill and comprehension erosion. In turn, Global Epistemic Threats (GETs) refer to the aggregate impact on collective justification and the transmission of epistemic error at institutional scale.

The recursive interplay between local and global threats is emphasized: local forfeiture of reflective epistemic duties by individuals diminishes collective rationality, while a downward shift in institutional or societal epistemic norms can further degrade individual standards—a dynamic conceptualized as local-to-global and global-to-local causal diffusion.

Figure 1: The epistemological model of human–LLM interaction, illustrating the two-way influences between individual reflective standards and collective rationality.

Proposed Multi-Tier Solution Framework

In response to these threats, the author advances a tripartite mitigatory architecture:

1. Normative Models for Individual Interaction: Advocating strategically informed, virtue-epistemology-guided interaction with LLMs, cultivating epistemic virtues (open-mindedness, intellectual courage, epistemic responsibility) and disincentivizing vices (gullibility, negligent reliance).
2. Organizational and Institutional Norms: Calling for collective norm-setting at the organizational/meso level, including explicit institutional guidelines, internal training, and the propagation of negative and positive sanctions for unvirtuous and virtuous interactions respectively.
3. Deontic Constraints and Legal Controls: Recommending formal, possibly legislative, constraints on LLM integration in high-stakes settings and hard-coded discursive norms at the model layer. Approaches include Constitutional AI, regulation of LLM deployment, and mandatory transparency in training and inference.

The proposed epistemological model is oriented towards maximizing epistemic virtues and institutional epistemic health. The author recognizes the inevitability of Hybrid Human-AI Intelligence (HHAI) systems but cautions that without enacted epistemic norms, such hybridity could diminish the justificatory robustness and self-correction at the core of human knowledge production.

Implications and Speculations on Future AI Systems

The theoretical implications are twofold. First, the author’s framework clarifies that no amount of performance gains in LLMs can obviate the qualitative distinction between reliabilist transmission and reflective knowledge—unless or until architectures emerge that endow agents with reflective access and self-evaluation over inferential processes. This has direct relevance to debates within XAI and the feasibility of explainability as a pathway to proto-reflective machine reasoning; the author maintains that unless models can internally represent and access their justifications, reflective knowledge remains uniquely human.

Second, the analysis suggests that epistemological health in technologically advanced societies will be determined less by performance improvements in AI and more by the integrity of epistemic division of labor, oversight, and the cultivation of virtue at both individual and institutional scales.

The author’s discussion anticipates that future research in AI might investigate mechanisms for integrating reflective capacities into machine architectures or for constructing socio-technical systems that guarantee the continued application of internalist standards by human agents. Without such developments, the utility of LLMs as knowledge intermediaries remains at the cost of eroding the very standards on which collective knowledge depends.

Conclusion

The paper rigorously reframes the epistemological status of LLMs within human knowledge institutions. It establishes that LLMs instantiate an externalist, reliabilist form of justification that is insufficient for knowledge as reflective epistemology demands. Human-LMM interaction, left unregulated, threatens to erode both individual and social standards for justification, learning, and reasoning, risking a cumulative atrophy of reflective rationality in collective epistemic systems. The mitigation framework provided is comprehensive and actionable, calling for multi-scale norm-setting, institutional controls, and legislative or technical constraints to preserve epistemic virtues. The theoretical distinctions and normative recommendations advanced are indispensable for guiding policy, organizational design, and the development of future AI systems concerned with epistemic integrity.

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Reference: "The Epistemological Consequences of LLMs: Rethinking collective intelligence and institutional knowledge" (2512.19570)