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Cited by 33Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning

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Mccradden, Melissa, Odusi, Oluwadara, Joshi, Shalmali, Akrout, Ismail, Ndlovu, Kagiso, Glocker, Ben, Maicas, Gabriel, Liu, Xiaoxuan +7 more·2023·2023 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3593013.3594096
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Abstract

The problem of algorithmic bias represents an ethical threat to the fair treatment of patients when their care involves machine learning (ML) models informing clinical decision-making. The design, development, testing, and integration of ML models therefore require a lifecycle approach to bias identification and mitigation efforts. Presently, most work focuses on the ML tool alone, neglecting the larger sociotechnical context in which these models operate. Moreover, the narrow focus on technical definitions of fairness must be integrated within the larger context of medical ethics in order to facilitate equitable care with ML. Drawing from principles of medical ethics, research ethics, feminist philosophy of science, and justice-based theories, we describe the Justice, Equity, Fairness, and Anti-Bias (JustEFAB) guideline intended to support the design, testing, validation, and clinical evaluation of ML models with respect to algorithmic fairness. This paper describes JustEFAB's development and vetting through multiple advisory groups and the lifecycle approach to addressing fairness in clinical ML tools. We present an ethical decision-making framework to support design and development, adjudication between ethical values as design choices, silent trial evaluation, and prospective clinical evaluation guided by medical ethics and social justice principles. We provide some preliminary considerations for oversight and safety to support ongoing attention to fairness issues. We envision this guideline as useful to many stakeholders, including ML developers, healthcare decision-makers, research ethics committees, regulators, and other parties who have interest in the fair and judicious use of clinical ML tools.

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Authors
Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., Maicas, G., Liu, X., Mazwi, M., Garnett, T., Oakden-Rayner, L., Alfred, M., Sihlahla, I., Shafei, O., & Goldenberg, A.
Affiliations
  • Hospital for Sick Children (CA)
  • University of Sheffield (GB)
  • Columbia University (US)
  • University of Botswana (BW)
  • Imperial College London (GB)
  • Australian Institute for Machine Learning, Australia
  • University of Birmingham (GB)
  • University of Toronto (CA)
  • University of Cape Town (ZA)
ORCID
Venue
2023 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2023-06-12
Pages
1505-1519
DOI
10.1145/3593013.3594096
OpenAlex
W4380318724
Cited by
33
References
89
Citations by year
2026: 12 · 2025: 8 · 2024: 11 · 2023: 2
FWCI
8.27
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
Funders
Hospital for Sick Children
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
algorithmic bias, algorithmic fairness, machine learning in healthcare, medical ethics, social justice, moral norms, bias mitigation, ethical decision-making framework, feminist philosophy of science, research ethics, health care equity

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