The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge
Angjelin Hila

TL;DR
This paper explores how large language models impact collective knowledge and epistemic standards, emphasizing the importance of maintaining reflective justification in human-LLM interactions to preserve epistemic integrity.
Contribution
It introduces a new epistemological framework distinguishing internalist and externalist justification, analyzing LLMs' role in collective epistemology, and proposes norms to mitigate epistemic risks.
Findings
LLMs approximate externalist reliabilism but lack reflective justification.
Outsourcing reflective work to LLMs can weaken epistemic standards.
Proposes a three-tier norm program to safeguard epistemic practices.
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…
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Taxonomy
TopicsEmbodied and Extended Cognition · Language and cultural evolution · Ethics and Social Impacts of AI
