An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Open accessAbstract
Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.
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AI systems can suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation.
“On the other hand, AI systems can also suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [7]
Bias particularly impacts disadvantaged populations, which can be subject to algorithmic predictions that are less accurate or underestimate the need for care.
“Bias particularly impacts disadvantaged populations, which can be subject to algorithmic predictions that are less accurate or underestimate the need for care.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [7]
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- Authors
- Yang, J., Soltan, A. A. S., Eyre, D. W., Yang, Y., & Clifton, D. A.
- Affiliations
- University of Oxford (GB)
- John Radcliffe Hospital (GB)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Yang, Jenny
- Venue
- npj Digital Medicine
- Published
- 2023-03-29
- Volume
- 6
- Issue
- 1
- Pages
- 55-55
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10050816
- OpenAlex
- W4361285150
- Cited by
- 159
- References
- 40
- Citations by year
- 2026: 44 · 2025: 66 · 2024: 38 · 2023: 11
- FWCI
- 39.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (0009350)
- Robertson Foundation
- University Hospitals Birmingham NHS Foundation Trust
- National Institute for Health Research Health Protection Research Unit
- National Institute for Health and Care Research (ACF-2020-13-015)
- Royal Academy of Engineering
- University of Oxford
- European Commission (HORIZON2020, 955681)
- Shanghai Jiao Tong University
- Oxford University Hospitals NHS Foundation Trust
- Medical Research Council (MC_PC_19049)
- 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
- adversarial training, algorithmic bias, machine learning in healthcare, fairness, equalized odds, COVID-19 prediction, ethnic prejudice, health disparities, external validation
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