Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations
Open accessAbstract
Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Our work makes three key contributions. First, we find that models can identify patient self-reported race from clinical notes even when the notes are stripped of explicit indicators of race. Second, we determine that human experts are not able to accurately predict patient race from the same redacted clinical notes. Finally, we demonstrate the potential harm of this implicit information in a simulation study, and show that models trained on these race-redacted clinical notes can still perpetuate existing biases in clinical treatment decisions.
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Algorithmic bias can result in patient harm through misdiagnosis, underdiagnosis, or unequal access to resources for certain demographic groups.
“For example, algorithmic bias can result in patient harm through misdiagnosis, underdiagnosis, or unequal access to resources for certain demographic groups18–20.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
The Introduction reports that a healthcare algorithm was less likely to refer Black patients to high-risk care management programs, supporting the unequal-access portion. Section 6.2 shows that a model trained on biased decisions was less likely to recommend treatment for Black patients. The paper does not establish misdiagnosis or underdiagnosis as outcomes of algorithmic bias; its treatment-recommendation experiment is a simulation, not a finding of actual patient harm. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGiven the required skills and expertise and roles and responsibilities of AI governance committee members described in the PPTO framework, participants nominated various personnel to be part of the AI governance committee.
“Given the required skills and expertise and roles and responsibilities of AI governance committee members described in the PPTO framework20, participants nominated various personnel to be part of the AI governance committee.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
The citation is attached to the PPTO framework’s description of AI governance committee members’ skills and responsibilities, not to the participants’ nominations. Our copy’s Discussion and Conclusion addresses fairness checks and limits of human oversight, but the passages retrieved for “PPTO framework” and “AI governance committee” do not describe that framework or its committee roles. No page number was available in the indexed passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredParticipants found the stakeholders outlined in the PPTO framework to be relevant, with clearly defined roles and responsibilities.
“Participants found the stakeholders outlined in the PPTO framework20 to be relevant, with clearly defined roles and responsibilities.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
Our copy’s Discussion and Conclusion addresses race prediction from clinical notes and its implications for care, not participants’ assessment of a stakeholder framework. Searches for “PPTO framework stakeholders roles responsibilities” and the claimed participant assessment found no passage addressing the PPTO framework or whether its roles were clearly defined. (LLM, conf=0.76)
Experimental — accuracy not yet measuredParticipants responded positively to the process outlined in the PPTO framework, finding it practical and aligned with the organizational needs.
“They responded positively to the process outlined in the PPTO framework20, finding it practical and aligned with the organizational needs.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
The Methods (section 5.1) describe recruiting physicians for an experiment, and the Results (section 5.2) report their accuracy in predicting patient race. Searches for “PPTO,” participant feedback, and organizational needs found no passage about a PPTO framework or participants finding its process practical. (LLM, conf=0.76)
Experimental — accuracy not yet measuredParticipants found technology infrastructure described in the PPTO framework relevant and agreed to use it to examine gaps in their existing system.
“Participants found technology infrastructure described in the PPTO framework20 relevant and agreed to use it to examine gaps in their existing system.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
The Methods section describes a web-based experiment with physicians, while the Introduction and Discussion address racial bias in clinical notes and machine-learning systems. Searches for the PPTO framework and for participants agreeing to examine gaps in an existing system found no passage addressing either point in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Participants found operational strategies described in the PPTO framework relevant.
“Participants found operational strategies described in the PPTO framework relevant20.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [20]
The Methods and Results of section 5 describe an experiment in which physicians guessed patients’ race from clinical notes; they do not report participants evaluating operational strategies. Searches for “PPTO framework” and “operational strategies” found no passage addressing whether participants considered those strategies relevant. No page numbers were available in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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Limiting bias in AI models for improved and equitable cancer care
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Self-citationHealth Equity in Clinical Research Informatics
Maurud, S., Henni, S. H., & Moen, A. (2023) · Yearbook of Medical Informatics · doi:10.1055/s-0043-1768720 · Cited by 8 · see citing works
Intersectionality and Testimonial Injustice in Medical Records
Andrews, K. S., Shah, B., & Cheng, L. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.39 · Cited by 7 · see citing works
Presentation matters for AI-generated clinical advice
Ghassemi, M. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01721-7 · Cited by 7 · see citing works
Self-citationRedefining Bias Audits for Generative AI in Health Care
Chen, I. Y., & Alsentzer, E. (2025) · NEJM AI · doi:10.1056/aip2500015 · Cited by 5 · see citing works
A machine learning model using clinical notes to identify physician fatigue
Hsu, C.-C., Obermeyer, Z., & Tan, C. (2025) · Nature Communications · doi:10.1038/s41467-025-60865-4 · Cited by 4 · see citing works
Agarwal, A., Banerjee, T., Romine, W. L., & Cajita, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825827 · Cited by 4 · see citing works
Causal thinking for decision making on Electronic Health Records: why and howPreprint
Doutreligne, M., Struja, T., Abécassis, J., Morgand, C., Celi, L. A., & Varoquaux, G. P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.01605 · Cited by 3 · see citing works
Self-citationIntegration of fairness-awareness into clinical language processing models
Abulibdeh, R., Lin, Y., Ahmadi, S., Sejdić, E., Celi, L. A., Zhao, Q., & Tu, K. (2026) · Communications Medicine · doi:10.1038/s43856-026-01433-9 · Cited by 2 · see citing works
Self-citationThe ‘Wild West’ of Medicine: Exploring the Emergence of ‘Grassroots’ AI Governance in Radiology
Ganesh, B., Schiff, D. S., & Anderson, S. (2025) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v8i2.36608 · Cited by 1 · see citing works
Understanding and Mitigating Unintended Bias in Medical AI Systems
Tyner-Monroe, S., Rakova, B., Kim, J. Y., Sendak, M., Balu, S., Tobey, D., Darling, C., Eigen, Z. J., Kloeppel, K., & Sharma, D. (2026) · Harvard Data Science Review · doi:10.1162/99608f92.59ca6018 · Cited by 1 · see citing works
Fairfax, F. G., & Wilder, J. M. (2026) · Social Science & Medicine · doi:10.1016/j.socscimed.2026.119505 · Cited by 1 · see citing works
Liao, W., & Joel Voldman, B. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000640 · Cited by 1 · see citing works
Debiasing patient embeddings for clinical applications
Brock, L. (2023) · doi:10.17760/d20560778
Establishing Vigilant Care: Data Infrastructures and the Black Birthing ExperiencePreprint
Mukogosi, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4872018
Navigating Bias When Using AI in Oncology
Thornber, K. (2024) · doi:10.33590/oncolamj/oawf4395
Schoene, A. M., Scharnetzki, L., DiBiase, J. F., Onaifo, A., Poon, Z., Strout, T. D., & Agarwal, I. (2024) · doi:10.2196/preprints.67807
Gerhardt, M., & Katz, A. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--55491
- Authors
- Adam, H., Yang, M. Y., Cato, K., Baldini, I., Senteio, C., Celi, L. A., Zeng, J., Singh, M., & Ghassemi, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Columbia University (US)
- IBM (United States) (US)
- Rutgers, The State University of New Jersey (US)
- Beth Israel Deaconess Medical Center (US)
- IBM Research - Cambridge (United States) (US)
- ORCID
- Venue
- Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2022-07-26
- Pages
- 7-21
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W4288058292
- Cited by
- 33
- References
- 44
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 11 · 2023: 14
- FWCI
- 7.70
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Helmholtz Professorship
- CIFAR Azreili Global Scholars
- National Institute of Health (NIBIB R01 grant EB017205)
- MIT Jameel Clinic
- National Institute of Nursing Leadership (R01NR016941-01 Communicating Narrative Concerns Entered by RNs (CONCERN))
- MIT-IBM Watson AI Lab
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical notes, racial bias, algorithmic fairness, algorithmic discrimination, racial minorities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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