Explainability and Privacy In Ai-Based Decision Support Systems: A Structured Literature Analysis

Artificial Intelligence is increasingly embedded in decision support systems that operate on sensitive or regulated data, intensifying demands for transparency, accountability, and privacy protection. In these contexts, explainable AI mechanisms aim to clarify how models produce outcomes, while privacy-preserving techniques such as differential privacy and federated learning seek to restrict the exposure of sensitive information. Reconciling these objectives has become a central challenge in AI-enabled information systems. This paper presents a structured literature analysis of the relationship between explainability and privacy-preserving mechanisms in decision support systems. The study investigates how prior research characterizes compatibility conditions, trade-offs, and the implications of explainability for trust and accountability under privacy constraints. The results show that explainability and privacy preservation are not inherently incompatible, but their coexistence is highly context-dependent. The literature indicates that global and aggregated explanations are generally more compatible with privacy-sensitive environments, whereas local and instance-level explanations tend to increase privacy risks. The findings also highlight that effective explanations must be aligned with stakeholder needs and governance requirements, rather than simply maximizing transparency. The paper contributes an integrated view of technical and socio-technical trade-offs and identifies directions for the design of trustworthy and privacy-aware decision support systems.