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REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Sayash Kapoor1,2, Emily M Cantrell3,4, Kenny Peng5

  • 1Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.

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This summary is machine-generated.

Failures in machine learning (ML) validity and reproducibility are common across sciences. The REFORMS checklist offers clear guidelines for conducting and reporting ML-based science to improve rigor and credibility.

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Area of Science:

  • Cross-disciplinary application of machine learning in scientific research.

Background:

  • Widespread adoption of machine learning (ML) methods in science is hindered by frequent failures in validity, reproducibility, and generalizability.
  • These failures impede scientific progress, foster consensus around incorrect findings, and damage the credibility of ML-driven research.
  • Consistent patterns of ML application failures are observed across diverse scientific disciplines.

Purpose of the Study:

  • To provide actionable recommendations for the rigorous application and transparent reporting of machine learning in scientific research.
  • To address the common pitfalls encountered in ML-based scientific studies across various fields.

Main Methods:

  • Development of the REFORMS checklist (Recommendations for Machine-learning-based Science) based on an extensive literature review.
  • Consensus-building process involving 19 researchers from computer science, data science, mathematics, social sciences, and biomedical sciences.
  • Checklist comprises 32 questions and accompanying guidelines.

Main Results:

  • The REFORMS checklist offers a structured approach to enhance the quality of ML-based scientific research.
  • Guidelines are designed to improve validity, reproducibility, and generalizability of ML applications.
  • The checklist was developed through a rigorous, interdisciplinary consensus process.

Conclusions:

  • The REFORMS checklist serves as a vital resource for researchers designing and executing studies.
  • It aids peer reviewers in evaluating the methodological soundness of ML-based research.
  • Journals can utilize REFORMS to enforce higher standards for transparency and reproducibility in scientific publications.