Automating the practice of science: Opportunities, challenges, and implications
Journal article, 2025
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.
automation
metascience
computational scientific discovery
AI for science
Author
Sebastian Musslick
Osnabrück University
Brown University
Laura K. Bartlett
London School of Economics and Political Science
Suyog H. Chandramouli
Princeton University
Aalto University
University of Alberta
Marina Dubova
Indiana University
Fernand Gobet
London School of Economics and Political Science
University of Roehampton
Thomas L. Griffiths
Princeton University
Jessica Hullman
Northwestern University
Ross King
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Cambridge
J. Nathan Kutz
University of Washington
Christopher G. Lucas
University of Edinburgh
Suhas Mahesh
University of Toronto
Franco Pestilli
University of Texas
Sabina J. Sloman
University of Manchester
William R. Holmes
Indiana University
Proceedings of the National Academy of Sciences of the United States of America
0027-8424 (ISSN) 1091-6490 (eISSN)
Vol. 122 5 e2401238121Subject Categories (SSIF 2025)
Computer Sciences
DOI
10.1073/pnas.2401238121
PubMed
39869810