AI ‐Assisted Scientific Figures in Scholarly Publishing: A Risk‐Based Taxonomy by Evidentiary Function
Year: 2026
Type: article
Abstract: Summary AI‐assisted figures should be assessed by what they claim to represent, not by AI use alone. Existing AI‐image policies may not offer enough granularity for diverse figure types in scholarly publishing. The taxonomy classifies figures as explanatory, analytical, simulated, or evidentiary. Each figure type carries distinct integrity risks and requires proportionate editorial safeguards. Figure‐level classification could improve disclosure, auditability, labeling, and preservation of original observations.
Source: Learned Publishing
DOI: 10.1002/leap.2085
Authors: Malcolm Koo, Ming‐Chi Lu
Institutions: University of Toronto, Dalin Tzu Chi Hospital, Tzu Chi University
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,080
OpenAlex ID: W7166358706