Explainable Artificial Intelligence for Reliable and Transparent Decision Support Systems
Keywords:
explainable artificial intelligence; decision support systems; reliability; transparency; governance; socio-technical infrastructure; fairness; accountability; deployment; lifecycle managementAbstract
The growing deployment of data-driven decision support systems across critical domains has intensified demands for transparency, accountability, and operational reliability. Explainable artificial intelligence has emerged as a socio-technical response to these demands, yet its integration into dependable decision support infrastructures remains uneven and conceptually fragmented. This paper examines explainable artificial intelligence from a systems perspective, focusing on structural trade-offs, architectural layering, governance mechanisms, deployment constraints, and lifecycle sustainability rather than on isolated algorithmic techniques. The discussion develops an integrated view in which explanation is treated not as a post hoc addition to an opaque model, but as a property distributed across data pipelines, model selection processes, interface design, organizational workflows, and regulatory environments. The paper analyzes how different explanation modalities interact with institutional decision practices, how reliability and robustness requirements shape explanation infrastructures, and how fairness and accountability obligations complicate the design of transparent systems. Cross-domain comparisons from clinical, financial, and public sector applications illustrate the importance of context-sensitive explanation strategies. The paper further addresses policy implications, including the rise of regulatory frameworks that demand meaningful transparency and human oversight. By connecting technical design choices to institutional and regulatory contexts, the analysis provides an interdisciplinary foundation for building decision support systems that are not only accurate but also legible, contestable, and sustainable. The result is a system-level research agenda that positions explainability as a core infrastructural capability for dependable artificial intelligence.
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