EthosPredict: A Human-Governed Framework for Fairness Arbitration in Privacy-Preserving Clinical Prediction
Abstract
Frameworks for trustworthy clinical artificial intelligence routinely require a model to satisfy several group-fairness criteria at once, alongside privacy preservation and human oversight. We argue that such requirements are not merely difficult to meet but are formally unsatisfiable, and that the standard response is the wrong one. Unequal outcome prevalence across subgroups is the normal condition in clinical populations, not a pathological case; any framework treating demographic parity, equalized odds, the false-negative-rate gap and disparate impact as jointly binding will therefore issue contradictory remediation demands without detecting that it has. EthosPredict treats metric incompatibility as the condition that makes human governance necessary, not as a defect governance must route around. We present three contributions. First, a constructed demonstration in which a classifier with a zero false-negative-rate gap and exact equalized odds is nonetheless flagged as disparate-impact-violating purely because prevalence differs, where forcing compliance would require inducing over-diagnosis in the lower-prevalence group. Second, a tiered arbitration protocol ordering fairness criteria by directness of patient harm instead of statistical convenience, making the false-negative-rate gap binding and treating demographic parity and disparate impact as diagnostics that route to human adjudication. Third, a mapping of each tier onto a named oversight role with defined authority and response window, with the failure modes of that human layer analysed against the clinical decision support literature. This is a conceptual and design contribution; no experimental evaluation is reported. Section VIII specifies a pre-registered protocol by which the framework's claims can be falsified.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
129-140 |
Published |
February 18, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Santhosh Kusuma Kumar Parimi (%2026). EthosPredict: A Human-Governed Framework for Fairness Arbitration in Privacy-Preserving Clinical Prediction. International Journal of Science, Research and Technology , Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 129-140. https://doi.org/10.15662/IJSRAT.2026.0901013 |
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C. (2025, July). Addressing Ethical Challenges in AI-Driven Health Predictions. In 2025 2nd International Conference on Computing and Data Science (ICCDS) (pp. 1-6). IEEE.
[22] Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung,
K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25, 1337–1340.
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[40] Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science, vol. 366, no. 6464, pp. 447–453, 2019.
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[42] M. Coots et al., “Racial bias in clinical and population health algorithms: A critical review of current debates,” Annual Review of Public Health, vol. 45, 2024.
[43] M. H. Chin et al., “Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care,” JAMA Network Open, vol. 6, no. 12, 2023.
[44] S. Siddique et al., “The impact of health care algorithms on racial and ethnic disparities,” Annals of Internal Medicine, vol. 177, no. 4, 2024.
[45] R. Eden et al., “A scoping review of the governance of federated learning in healthcare,” npj Digital Medicine, vol. 8, 2025.
[46] P. Bailo et al., “Governing healthcare AI in the real world: How fairness, transparency, and human oversight can coexist: A narrative review,” 2026.
[47] B. A. Mir et al., “Federated learning in healthcare ethics: A systematic review of privacy-preserving and equitable medical AI,” Healthcare, 2026.
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[51] S. Pati et al., “Privacy preservation for federated learning in health care,” Patterns, vol. 5, no. 7, 2024.
[52] H. Yin et al., “Do gradient inversion attacks make federated learning unsafe?” IEEE Transactions on Medical Imaging, vol. 41, no. 7, pp. 1913–1924, 2022.
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[58] K. C. Nanji et al., “Medication-related clinical decision support alert overrides in inpatients,” Journal of the American Medical Informatics Association, vol. 25, no. 5, pp. 476–481, 2018.
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