Balancing Privacy and Explainability in Healthcare AI through Secure Multi-Party Computation and Local Interpretable Model-Agnostic Explanations
DOI:
https://doi.org/10.66280/cis.v4i1.131Abstract
The rapid integration of artificial intelligence within healthcare systems has precipitated a fundamental tension between the necessity for stringent data privacy and the clinical requirement for model interpretability. While advanced deep learning architectures offer unprecedented diagnostic accuracy, their inherent opacity often clashes with the ethical and legal mandates for transparency in medical decision-making. Simultaneously, the sensitive nature of patient health information necessitates robust protection against data leakage, often complicating the deployment of centralized transparency tools. This research paper explores a system-level synthesis of Secure Multi-Party Computation and Local Interpretable Model-Agnostic Explanations to bridge this gap. By leveraging cryptographic protocols to facilitate collaborative computation without exposing underlying datasets and integrating post-hoc explanation frameworks to clarify model behavior, this study proposes a robust architectural paradigm for trustworthy healthcare AI. The discussion emphasizes the structural trade-offs between computational overhead and clinical utility, the governance frameworks required for decentralized auditing, and the policy implications for data sovereignty in international medical research. Ultimately, the paper argues that the sustainability of healthcare AI depends on a socio-technical approach that treats privacy and explainability as mutually reinforcing objectives rather than zero-sum constraints, providing a comprehensive roadmap for deploying secure and interpretable diagnostic infrastructures in diverse clinical environments.
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