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Overview of Artificial Intelligence touchpoints with multi-criteria decision analysis

Bekas Ioannis

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Summary

The integration of artificial intelligence (AI) in multi-criteria decision analysis (MCDA) and vice versa has been an increasingly active area of research in recent years and has become an interesting topic to researchers who work in the Data Science field. The development of AI technologies has the potential to significantly alter how businesses and organizations now operate and we study how they affect Decision Making in the operational field.This literature review aims to examine the connection points between AI and MCDA and to provide an in-depth presentation of the contribution of AI in the field of Decision Making. Business managers, technology engineers, developers, analysts and implementers of policies must implement Multi-Criteria Decision Making (MCDM) Models to select their final decisions among all the available alternatives and criteria therefore the establishment of new AI technologies in the Decision-Making field is of high interest.The methodological approach of the thesis's report is based on valid bibliographic sources analysis. A thorough literature search was done utilizing both electronic databases and manual searches of conference proceedings, books and journals. The review is structured around three main topics: the use of AI techniques for MCDA, the integration of MCDA and AI for decision-making, and the ethical implications of combining AI and MCDA.The review's key findings show that AI technologies, Decision Making Models, and Information Systems, may be successfully integrated with MCDA to enhance decision-making outcomes. Machine learning algorithms may be utilized to optimize the process of analysing many capabilities based on multiple criteria, and AI-powered decision support systems can give decision-makers with invaluable help.This analysis concludes that the integration of AI technologies with MCDA has the potential to considerably improve the efficacy and efficiency of decision-making procedures. To fully comprehend the possibilities of this integration and determine the most effective methods for using AI technology in MCDA, more study is required. Finally, another key finding is that the unique viewpoint that human experience and knowledge offer to decision-making cannot be replaced by AI models alone. This involves an awareness of the situation's background and complexities, as well as the capacity to weigh intangible issues such as ethical and societal considerations.

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