Machine learning modeling practices to support the principles of AI and ethics in nutrition research
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Authors
Thomas, Diana M.
Kleinberg, Samantha
Brown, Andrew
Crow, Mason
Bastian, Nathaniel D.
Reisweber, Nicholas A.
Lasater, Robert
Kendall, Thomas
Shafto, Patrick
Blaine, Raymond W.
Issue Date
2022
Type
Journal articles
Language
Keywords
Ethics , Center for Data Analysis and Statistics
Alternative Title
Abstract
Background
Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.
Methods
Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages.
Results
Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research.
Conclusion
The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.
Description
Citation
Thomas, Diana M., Samantha Kleinberg, Andrew W. Brown, Mason Crow, Nathaniel D. Bastian, Nicholas Reisweber, Robert Lasater, et al. 2022. “Machine Learning Modeling Practices to Support the Principles of AI and Ethics in Nutrition Research.” Nutrition & Diabetes. Springer Science and Business Media LLC. https://doi.org/10.1038/s41387-022-00226-y.
Publisher
Nutrition & Diabetes