Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review

Abstract

This survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As Artificial Intelligence (AI) continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. This survey examines the methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based Decision Support Systems. Through an in-depth analysis of current research and practical implementations, this article aims to guide researchers, practitioners, and decision-makers in navigating the intricate landscape of XAI-based DSSs. These systems assist end-users in their decision-making, providing a full picture of how a decision was made and boosting trust. Furthermore, a methodical taxonomy of the current methodologies is proposed and representative works are presented and discussed. The analysis of recent studies reveals that there is a growing interest in applying XDSSs in fields such as medical diagnosis, manufacturing, and education, to name a few, since they smooth down the trade-off between accuracy and explainability, boost confidence, and also validate decisions.

Ähnliche Arbeiten