Scientific Muse and Misuse: Reevaluating Authorship Attribution and Liability Allocation in the Generative AI Age
Recent research on generative artificial intelligence has primarily focused on two separate issues: (1) the attribution of copyright authorship and ownership, and (2) the allocation of liability for harms resulting from artificial intelligence (“AI”) outputs. However, there is a significant but often-overlooked dimension: the interplay between authorship attribution and liability allocation in assisted scientific research. Therefore, this Article examines the similarities and differences between intellectual property and tort law, highlighting how generative AI challenges long-standing assumptions in both fields and encouraging a reevaluation of scientific standards, liability regimes, and governance of AI.
Drawing on comparative legal analysis, ethical guidelines, and a case study of MIT’s AI-driven antibiotic discovery, this Article develops a unified analytical framework for intellectual property and tort law that positions “control” as the cornerstone of both authorship and liability. This framework reveals how different actors— researchers, institutions, AI developers, and AI companies—exercise varying degrees of control over AI-assisted scientific research. This Article does not suggest that AI itself should be recognized as an author, but it contemplates the circumstances in which it may be appropriate for AI companies and developers to be acknowledged as co-authors and, accordingly, bear liability for misconduct.
This Article argues for developing a unified analytical framework that bridges the gap between copyright and tort law. Such a framework would provide policymakers, scientific institutions, and academic journals with a comprehensive toolkit for rethinking current authorship criteria, liability regimes, and ethical guidelines. It would safeguard incentives for innovation while ensuring accountability in the ever-evolving technological landscape.
PDF: https://journals.law.unc.edu/ncjolt/wp-content/uploads/sites/4/2025/10/Ganot_ScientificMuse_Publish_Go.pdf
Author: Inbar Cohen Ganot
Volume 27, Issue 1