Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Explainable Decision Support Systems (XDSS) have emerged as a critical tool for integrating artificial intelligence (AI) into decision-making processes, combining predictive accuracy with interpretability to foster user trust and accountability. Despite their increasing adoption across various domains, XDSS face significant computational challenges, including data complexity, scalability, real-time processing demands, and ensuring fairness and robustness. These challenges are further compounded by the unique requirements and constraints of different application areas, which directly influence system performance and utility, making the strategic selection of application areas a crucial step in optimizing XDSS performance. Therefore, this paper employs an adaptation of Multi-Criteria Decision Analysis (MCDA) to systematically evaluate and rank potential application areas based on domain-specific factors such as data characteristics, explanation requirements, and computational constraints. Through a detailed analysis of challenges and application contexts, this paper underscores the importance of domain selection in maximizing the practical utility and computational efficiency of XDSS. The findings emphasize that selecting the right application area is foundational to ensuring XDSS efficiency and highlight how the MCDA framework can be extended to support further configuration decisions within selected domains. This paper contributes to the strategic planning and development of future XDSS frameworks, offering guidance for developers and business leaders aiming to implement these systems more effectively.
