Authorship Without Writing: Large Language Models and the Senior Author Analogy

2025-09-05

AI Summary

This paper addresses the contested authorship status of large language models (LLMs) in scientific writing. Motivated by the absence of consensus in bioethics and publishing guidelines regarding AI eligibility for authorship, the study employs philosophical analysis grounded in authoritative authorship criteria—particularly those of the International Committee of Medical Journal Editors (ICMJE). It proposes, for the first time, an analogical framework: LLM generation of complete manuscript drafts is conceptually equivalent to the supervisory contributions of senior authors—namely, defining research scope, ensuring scientific integrity, and upholding scholarly accountability. This analogy implies that if current norms recognize non-executional, high-level intellectual contributions as sufficient for authorship, then systematic LLM text generation likewise qualifies; otherwise, foundational revisions to authorship criteria are required. The work challenges entrenched authorship paradigms and provides a principled ethical framework and normative basis for updating academic attribution policies in the AI era.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Debating authorship legitimacy for human-LLM collaborative writing
• Comparing LLM use to senior author roles in research teams
• Reconciling AI-generated content with existing authorship criteria

Innovation

• LLMs analogous to senior authorship
• LLMs generate complete research drafts
• Legitimate authorship under specific conditions