The integration of Artificial Intelligence with Multi-criteria Decision-Making/Aiding Methods: a systematic literature review

Source abstract

Abstract Artificial Intelligence (AI) has advanced significantly in the 21st century, evolving into a crucial tool for decision-making. A prominent trend is its integration with Multi-Criteria Decision-Making/Aiding (MCDM/A) methods to support complex decisions across diverse engineering domains. This paper presents a systematic literature review analyzing 111 papers from scientific databases on integrating AI with MCDM/A methods. Unlike prior reviews that primarily catalogue methods or hybrid techniques, this study introduces a socio-technical analytical framework comprising three layers—technical configurations, functional mechanisms, and human–AI collaboration patterns—to explain how and why AI reshapes multicriteria decision processes. The findings reveal recurrent architectural patterns, identify dominant functional roles of AI across decision-process phases, and uncover an emerging shift from automation-oriented systems toward augmentation-based decision support. Rather than providing a purely descriptive mapping of the literature, this review undertakes an investigative task guided by a socio-technical framework. By examining how structural configurations of AI–MCDM/A integration reshape the stages of the decision process and redistribute roles between humans and AI, the study moves beyond cataloguing techniques to uncover underlying integration logics, structural tensions, and developmental trajectories. In doing so, it offers both a conceptual consolidation for scholars and a structured foundation for the design of next-generation, human-centered intelligent decision support systems (IDSS).