A Unified Framework for Explainable and Privacy-Preserving Machine Learning in Real-Time Decision-Making Systems

Abstract

Machine learning (ML) has revolutionized real-time decision-making, enabling significant advancements in fields such as healthcare, cybersecurity, and autonomous systems. Despite these strides, two critical challenges hinder broader adoption: the lack of transparency in complex models and concerns about preserving user privacy. This paper introduces a comprehensive framework that combines explainable artificial intelligence (XAI) with privacy-preserving machine learning (PPML), tailored for systems requiring real-time responsiveness. The framework employs innovative methods, including interpretable modeling, federated learning, and secure computation, to balance accuracy with ethical considerations. Rigorous evaluations on benchmark datasets, particularly in healthcare and energy optimization, highlight its effectiveness, with results demonstrating a 15% increase in interpretability scores and 20% enhancement in privacy adherence compared to existing approaches. By addressing these critical barriers, the proposed framework establishes a foundation for integrating ML ethically and efficiently into real-time applications.