Navigating the Privacy-Explainability-Utility Trilemma: A Survey of Differential Privacy and Explainable AI

Artificial Intelligence (AI) systems used in sensitive domains must balance privacy protection and explainability. Differential Privacy (DP) provides formal privacy guarantees, while Explainable AI (XAI) aims to make model decisions understandable. However, privacy-preserving noise often degrades explanation quality, making their combination challenging. This survey provides a structured review of literature published between 2020 and 2025 and differs from prior threat-oriented or benchmark-driven surveys by adopting a mechanism-centered perspective that explicitly examines how DP techniques interact with different explanation paradigms. From this broader technical and system design perspective, we analyze DP and XAI interactions beyond purely threat-oriented considerations. We analyze these interactions through the lens of the Privacy-Explainability-Utility trade-off, which shows that improving privacy can harm either model performance or explanation quality. To help organize and advance this growing field, we propose a five-category taxonomy that characterizes how DP and XAI connect in machine learning systems. We also introduce the Differential Privacy-Explainable AI (DP-XAI) Interaction Matrix, which maps combinations of privacy techniques and explanation methods that have been studied. Our analysis identifies several research gaps, especially in concept-based explanations and underused architectures, and highlights concrete directions for future research on building machine learning systems that are both privacy-protected and meaningfully explainable.