Sayash Kapoor, Emily M Cantrell, Kenny Peng, Thanh Hien Pham, Christopher A Bail, Odd Erik Gundersen, Jake M Hofman, Jessica Hullman, Michael A Lones, Momin M Malik, Priyanka Nanayakkara, Russell A Poldrack, Inioluwa Deborah Raji, Michael Roberts, Matthew J Salganik, Marta Serra-Garcia, Brandon M Stewart, Gilles Vandewiele, Arvind Narayanan
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science...
May 3, 2024: Science Advances