The Data Artifacts Glossary : a community-based repository for bias on health datasets
Gameiro, Rodrigo R. (Harvard T.H. Chan School of Public Health (Boston, Estats Units d'Amèrica))
Woite, Naira Link (Massachusetts Institute of Technology. Laboratory for Computational Physiology)
Sauer, Christopher M. (University Hospital Essen (Alemanya))
Hao, Sicheng (Duke University. Division of Pulmonary, Allergy, and Critical Care Medicine)
Fernandes, Chrystinne (Massachusetts Institute of Technology. Laboratory for Computational Physiology)
Premo, Anna E. (University of Pittsburgh. Learning Research and Development Center)
Teixeira, Alice Rangel (Universitat Autònoma de Barcelona. Departament de Filosofia)
Resli, Isabelle (Oregon State University. School of Electrical Engineering and Computer Science)
Wong, An-Kwok Ian (Duke University. Division of Pulmonary, Allergy, and Critical Care Medicine)
Celi, Leo Anthony (Harvard T.H. Chan School of Public Health (Boston, Estats Units d'Amèrica))

Data:	2025
Resum:	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. 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. 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. 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. 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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Bias ; Health equity ; Dataset ; Data Artifacts Glossary ; Artificial intelligence ; Machine learning
Publicat a:	Journal of Biomedical Science, Vol. 32, art 14 (february 2025) , ISSN 1423-0127

DOI: 10.1186/s12929-024-01106-6
PMID: 39901158

9 p, 2.4 MB

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