@article{204876,
  author = {Sayash Kapoor and Emily Cantrell and Kenny Peng and Thanh Hien and Christopher Bai and Odd Erik and Jake Hofman and Jessica Hullman and Michael Lones and Momin MalikMayo and Priyanka Nanayakkara and Russell Poldrack and Deborah Raji and Michael Roberts and Matthew Salganik and Marta Serra-Garcia and Brandon M. Stewart and Gilles Vandewiele and Arvind Narayanan},
  title = {REFORMS: Reporting Standards for ML-based Science},
  abstract = {<p>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 reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.</p>
},
  year = {2023},
  url = {https://reforms.cs.princeton.edu/$\#$about},
}