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STAR-ML

STAR-ML: A Rapid Screening Tool for Assessing Reporting of Machine Learning in Research

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STAR-ML is a generalized tool that can assess the quality of the reporting of Machine Learning (ML) in research articles quickly and consistently. This new tool will allow for filtering ML-related papers that can be included in a systematic or scoping review by ensuring transparent, reproducible, and correct screening of research for inclusion in the review article. It can also be utilized as a guideline when drafting a new manuscript to improve the quality of ML technique reported. 

We are currently conducting a study to validate STAR-ML in a larger population and are currently looking for individuals with varying degrees of experience. If this is of interest to you, please click HERE and follow the steps outlined for you!

To be able to participate in the study:

  • You should be 18 years or older
  • Have a basic idea of ML
  • Have a Bachelor's degree or higher, or be currently enrolled in an undergraduate granting institution and
  • Be able to read and understand English

Please note that participation is voluntary. This web-based survey is anonymous and confidential. No one will be able to match identities to individual survey responses.

The survey will take approximately 50 to 60 minutes to complete. To thank you for your time and acknowledge your contribution to this collective effort, you will be able to be entered into a draw for one of the 10 $50 Gift cards at the end of the study.

Take the Survey

To learn more about this study, please contact Md Asif Khan at or Dr. Ryan Koh at .

This study has been reviewed and has received ethics clearance through the Research Ethics Board at McMaster University (MREB# 6206) and UHN (REB# 22-5822).

Relevant Publications:

  1. Khan, M. A., Koh, R. G. L., Hassan, S., Liu, T., Tucci, V., Kumbhare, D., & Doyle, T. E. (2022, September). STAR-ML: A Rapid Screening Tool for Assessing Reporting of Machine Learning in Research. In 2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, NS, Canada, 2022, pp. 336-341, doi: 10.1109/CCECE49351.2022.9918312.
  2. Koh, R. G. L., Khan, M. A., Rashidiani, S., Hassan, S., Liu, T., Tucci, V., Kumbhare, D., & Doyle, T. E. Check It Before You Wreck It: A Guide to STAR-ML for Screening Machine Learning Reporting in Research. In IEEE Access, vol. 11, pp. 101567-101579, 2023, doi: 10.1109/ACCESS.2023.3316019.