aceThe ethics-driven data governance framework for health-based AI (EDG-AI4H): an integrated approach for empowering explainable AI in health insurance risk prediction in Iran
Purpose: The digital transformation of health data has enabled predictive models in insurance. In Iran, the absence of an indigenous data governance framework integrating ethical, legal, and technical considerations poses serious risks to responsible AI development. This study aimed to design an ethics-driven data governance framework to empower Explainable AI (XAI) in health insurance risk prediction in Iran. Methods: This qualitative study (2024) used thematic analysis and framework development. Data came from…
A four-layer governance framework with mandatory explainability and bias monitoring offers a roadmap for transparent, trustworthy AI risk prediction in Iran's health insurance sector.
The framework's operational feasibility has not been tested and requires empirical validation through future pilot implementation studies.
Evidence
- Peer-reviewedDialogues in Health2026-09-16
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Truvace Impact Record TRV-2026-1602, v1: “The ethics-driven data governance framework for health-based AI (EDG-AI4H): an integrated approach for empowering explainable AI in health insurance risk prediction in Iran.” Truvace, 2026-10-16. /record/TRV-2026-1602 (accessed at citation time). sha256 ef5cd6e61052c36e…
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