The Data Artifacts Glossary: a community-based repository for bias on health datasets
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12929_2024_Article_1106.pdf
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2.43 MB
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Adobe PDF
Checksum (MD5)
2859aebf6f6a3201be0ea3f9b6f64984
Author(s)
Gameiro, Rodrigo R.
Woite, Naira L.
Sauer, Christopher M.
Hao, Sicheng
Fernandes, Chrystinne O.
Premo, Anna E.
Teixeira, Alice R.
Resli, Isabelle
Wong, An-Kwok I.
Celi, Leo A.
Date Issued
February 4, 2025
Journal
Journal of Biomedical Science
Publisher
BioMed Central
Citation
Gameiro, R.R., Woite, N.L., Sauer, C.M. et al. The Data Artifacts Glossary: a community-based repository for bias on health datasets. J Biomed Sci 32, 14 (2025).
Version
Final published version
Abstract
Background The deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fairness of AI are critically dependent on the data it learns from. Biased datasets can lead to AI outputs that perpetuate disparities, particularly affecting social minorities and marginalized groups. Objective This paper introduces the “Data Artifacts Glossary”, a dynamic, open-source framework designed to systematically document and update potential biases in healthcare datasets. The aim is to provide a comprehensive tool that enhances the transparency and accuracy of AI applications in healthcare and contributes to understanding and addressing health inequities. Methods Utilizing a methodology inspired by the Delphi method, a diverse team of experts conducted iterative rounds of discussions and literature reviews. The team synthesized insights to develop a comprehensive list of bias categories and designed the glossary’s structure. The Data Artifacts Glossary was piloted using the MIMIC-IV dataset to validate its utility and structure. Results The Data Artifacts Glossary adopts a collaborative approach modeled on successful open-source projects like Linux and Python. Hosted on GitHub, it utilizes robust version control and collaborative features, allowing stakeholders from diverse backgrounds to contribute. Through a rigorous peer review process managed by community members, the glossary ensures the continual refinement and accuracy of its contents. The implementation of the Data Artifacts Glossary with the MIMIC-IV dataset illustrates its utility. It categorizes biases, and facilitates their identification and understanding. Conclusion The Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s12929-024-01106-6
