Framework for Ethical LLM-Assisted Scientific Research

Large language models are increasingly used in scientific research for tasks like literature review, hypothesis generation, and coding. This raises a fundamental question about the epistemic legitimacy of knowledge claims when parts of the reasoning process are delegated to an AI system. This paper introduces a normative and conceptual framework to analyze such delegation. The framework views scientific reasoning as a distributed process where contributions can originate from either humans or machines, but the ultimate responsibility for accepting these contributions into the scientific record remains human. It defines constructs such as content origin, human verification, responsibility assignment, accountable human ownership, and epistemic outcome to differentiate the source of a claim from its validation process and the human accountability attached to it. The core argument is that the ethical limits of LLM-assisted research are primarily determined by adequate human verification and accountable ownership, rather than by the extent of machine involvement itself. Based on this, the paper develops the concept of an "epistemic audit," a structured record detailing delegation, verification, provenance, and responsibility, designed to make AI-assisted reasoning transparent and reviewable. This framework provides a formal vocabulary for distinguishing responsible cognitive delegation from the neglect of epistemic responsibility in scientific inquiry.