Assertion, Accountability, and Large Language Models

Abstract Large language models (LLMs) increasingly participate in communicative practices that resemble human interaction: users ask them questions, rely on their outputs for belief formation and action guidance, and sometimes develop affective attachments. These practices raise a central philosophical question: can the outputs of LLMs be regarded as assertions, and if so, what follows for responsibility and accountability? Standard theories of assertion and testimony assume that assertions require asserters—agents endowed with intentionality, understanding, and belief—properties that contemporary LLMs plausibly lack. This paper challenges that assumption by disentangling assertion from asserter. While defending a conservative, properties-based account of LLM ontology—according to which they are neither speakers nor moral agents—it argues that their outputs can nevertheless function as assertions within communicative practice. The core claim is that assertoric function can persist independently of assertoric authority, giving rise to a structurally asymmetric case of assertion without asserter. To explain this asymmetry, the paper advances a pragmatic framework that combines a properties-based analysis at the level of ontology with a relational account at the level of interaction. This framework clarifies how LLM outputs can exert normative pressure on users while lacking a bearer of responsibility, thereby explaining the emergence of epistemic responsibility gaps without attributing moral or legal agency to artificial systems.