Authorship Without Writing: Large Language Models and the Senior Author Analogy
This addresses authorship attribution problems for researchers and institutions using AI tools, but it is incremental as it builds on existing debates without introducing new empirical data.
The paper tackles the controversy over whether humans using large language models (LLMs) can be considered authors in bioethical, scientific, and medical writing, arguing that LLM use under specific conditions is analogous to senior authorship, where individuals guide projects without writing, and concludes that such use should be recognized as legitimate or authorship criteria need revision.
The use of large language models (LLMs) in bioethical, scientific, and medical writing remains controversial. While there is broad agreement in some circles that LLMs cannot count as authors, there is no consensus about whether and how humans using LLMs can count as authors. In many fields, authorship is distributed among large teams of researchers, some of whom, including paradigmatic senior authors who guide and determine the scope of a project and ultimately vouch for its integrity, may not write a single word. In this paper, we argue that LLM use (under specific conditions) is analogous to a form of senior authorship. On this view, the use of LLMs, even to generate complete drafts of research papers, can be considered a legitimate form of authorship according to the accepted criteria in many fields. We conclude that either such use should be recognized as legitimate, or current criteria for authorship require fundamental revision. AI use declaration: GPT-5 was used to help format Box 1. AI was not used for any other part of the preparation or writing of this manuscript.