The Data Artifacts Glossary: a community-based repository for bias on health datasets
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 t...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Journal of Biomedical Science |
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| Online Access: | https://doi.org/10.1186/s12929-024-01106-6 |
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| author | Rodrigo R. Gameiro Naira Link Woite Christopher M. Sauer Sicheng Hao Chrystinne Oliveira Fernandes Anna E. Premo Alice Rangel Teixeira Isabelle Resli An-Kwok Ian Wong Leo Anthony Celi |
| author_facet | Rodrigo R. Gameiro Naira Link Woite Christopher M. Sauer Sicheng Hao Chrystinne Oliveira Fernandes Anna E. Premo Alice Rangel Teixeira Isabelle Resli An-Kwok Ian Wong Leo Anthony Celi |
| author_sort | Rodrigo R. Gameiro |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-11b22dc444284626a5789a7067a3ef45 |
| institution | Kabale University |
| issn | 1423-0127 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Science |
| spelling | doaj-art-11b22dc444284626a5789a7067a3ef452025-02-09T12:48:55ZengBMCJournal of Biomedical Science1423-01272025-02-013211910.1186/s12929-024-01106-6The Data Artifacts Glossary: a community-based repository for bias on health datasetsRodrigo R. Gameiro0Naira Link Woite1Christopher M. Sauer2Sicheng Hao3Chrystinne Oliveira Fernandes4Anna E. Premo5Alice Rangel Teixeira6Isabelle Resli7An-Kwok Ian Wong8Leo Anthony Celi9Laboratory for Computational Physiology, Massachusetts Institute of TechnologyLaboratory for Computational Physiology, Massachusetts Institute of TechnologyLaboratory for Computational Physiology, Massachusetts Institute of TechnologyDivision of Pulmonary, Allergy, and Critical Care Medicine, Duke UniversityLaboratory for Computational Physiology, Massachusetts Institute of TechnologyLearning Research and Development Center, University of PittsburghDepartment of Philosophy, Universitat Autónoma de BarcelonaSchool of Electrical Engineering and Computer Science, Oregon State UniversityDivision of Pulmonary, Allergy, and Critical Care Medicine, Duke UniversityLaboratory for Computational Physiology, Massachusetts Institute of TechnologyAbstract 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.https://doi.org/10.1186/s12929-024-01106-6BiasHealth equityDatasetData Artifacts GlossaryArtificial intelligenceMachine learning |
| spellingShingle | Rodrigo R. Gameiro Naira Link Woite Christopher M. Sauer Sicheng Hao Chrystinne Oliveira Fernandes Anna E. Premo Alice Rangel Teixeira Isabelle Resli An-Kwok Ian Wong Leo Anthony Celi The Data Artifacts Glossary: a community-based repository for bias on health datasets Journal of Biomedical Science Bias Health equity Dataset Data Artifacts Glossary Artificial intelligence Machine learning |
| title | The Data Artifacts Glossary: a community-based repository for bias on health datasets |
| title_full | The Data Artifacts Glossary: a community-based repository for bias on health datasets |
| title_fullStr | The Data Artifacts Glossary: a community-based repository for bias on health datasets |
| title_full_unstemmed | The Data Artifacts Glossary: a community-based repository for bias on health datasets |
| title_short | The Data Artifacts Glossary: a community-based repository for bias on health datasets |
| title_sort | data artifacts glossary a community based repository for bias on health datasets |
| topic | Bias Health equity Dataset Data Artifacts Glossary Artificial intelligence Machine learning |
| url | https://doi.org/10.1186/s12929-024-01106-6 |
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