LLMs are becoming routine tools in scientific research, assisting with literature synthesis, hypothesis generation, coding, and formal reasoning. Their use raises a central epistemic question: when parts of scientific reasoning are handed over to an artificial system, what conditions must remain under human control for knowledge claims to retain epistemic legitimacy and accountable authorship?
This paper develops a normative conceptual framework to analyze such delegation. Scientific reasoning is treated as a distributed process where contributions may originate from humans or machines, yet the responsibility for admitting them into the scientific record remains human. The framework distinguishes content origin $O(g)$, completion of human verification $V(g)$, responsibility assignment $R(g)$, accountable human ownership $M(g)$, and epistemic outcome $E(g)$. These constructs separate the provenance of a claim, the checking process, the epistemic result of that checking, and the human responsibility attached to its disposition.
The central proposition is that the ethical boundary of LLM‑assisted research is determined primarily by adequate verification and accountable human ownership rather than the degree of machine involvement. On this basis, the paper introduces the notion of an "epistemic audit": a structured record of delegation, verification, provenance, and responsibility intended to make AI‑assisted reasoning transparent and reviewable. The framework provides a formal vocabulary for distinguishing responsible cognitive delegation from the transfer or neglect of epistemic responsibility in scientific research.
Blogger's Review: The framework offers a clear operational guide for research ethics in the AI era, emphasizing verification and human ownership as crucial safeguards against the loss of epistemic accountability.