Background: As Artificial Intelligence (AI) systems become integral to high-stakes decision-making in healthcare, finance, and education, the opacity of "black box" models presents significant ethical and legal challenges. While deep learning models offer superior predictive accuracy, their lack of interpretability undermines user trust and complicates compliance with emerging privacy regulations.

Methods: This study employs a comprehensive theoretical analysis, synthesizing literature on Cyber Security, AI Governance, and Explainable AI (XAI). We utilize a systems-design perspective to construct a multi-layered framework that aligns algorithmic transparency with ethical privacy requirements. The research evaluates various XAI approaches—including prototype-based reasoning and counterfactual explanations—against metrics of fairness, privacy preservation, and explanation quality.

Results: Our analysis identifies a critical "Transparency-Privacy Paradox," where granular explanations may inadvertently leak sensitive training data. Furthermore, we find that post-hoc explanation methods often suffer from "deceptive transparency," providing plausible but unfaithful justifications for model behavior. Conversely, interpretable-by-design architectures demonstrate a higher capacity for building sustainable trust without compromising privacy standards.

Conclusion: We conclude that trust in AI cannot be achieved through performance metrics alone. Instead, it requires a holistic governance strategy that integrates "Privacy by Design" with robust XAI mechanisms. We propose a shift from retrospective explanation to prospective, interpretable model architecture as the standard for critical infrastructure.

Explainable AI, AI Governance, Algorithmic Fairness, Privacy by Design

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