Rembrandt through the eyes of the machine: Toward explainable Human-AI collaboration in painting attribution.

Authors

Andrés Arias-Rosales, Arthur K Wheelock, J Kenneth Moritz, Philip R LeDuc, Jonathan Cagan

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 45. Pages e2626989123. Nov 10, 2026. Epub Oct 26, 2026.

Abstract

With questions that persist for centuries, art attribution is a complex detective effort that shapes cultural identity. Our goal is to complement human expertise, providing statistical and visual arguments that shed new light on discussions about great masters like Rembrandt and beyond, with long-term implications for art analysis and human-AI collaboration. We present an AI-driven explainable approach to assist painting attribution with up to 97.9% recall, 95.8% accuracy when highly confident, and methods that identify the most relevant areas. This work supports human-AI collaboration by incorporating expert guidance, expanding the outputs with explainable visualizations, tailoring settings to users' needs, thorough calibrations to avoid overconfidence, and building a dataset that balances quality with quantity and focuses on comparable artists like a master's pupils and teachers.

PMID:
42932626
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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