Multi-criteria consensus-based group decision making with prospect-regret TOPSIS for human-AI tool selection under linguistic Z-numbers.

The rapid advances in artificial intelligence (AI) and machine learning have transformed computer technology. Now, computers can learn and adapt autonomously, make sound judgments, and create environments in which humans and AI work in harmony. Analytical frameworks that can process data with uncertainty, imprecision, and multiple perspectives are essential for the success of human-AI collaboration. In this connection, linguistic Z-number (LZN) evolution models play a crucial role in selecting tools for human-AI collaboration because they can better capture uncertainty and fuzziness. Given this, we extend multicriteria group decision-making (MCGDM) by incorporating LZNs. The structure of MCGDM under LZNs, the consensus-building process, estimation of experts' weights, and the ranking of options are the main challenges. In the experts' primary opinion-based MCGDM, we believe that expert participation and the opportunity to revise their opinions with original linguistic terms (LTs) would be more practical. However, in existing studies, experts have no scope to revise their opinions based on the original LTs. Most studies on consensus-building either do so automatically or advise experts to revise their opinions based on virtual LTs. But either automatically or virtually, LTs cannot be physically represented by words, which is a significant drawback. Given these facts, we propose an interactive, strategy-based consensus-building in which we advise experts to revise their opinions within the original LTs. To estimate experts' weights, we developed an integrated subjective and objective method. By simultaneously assigning subjective weights and opinion-based relative-relevance indices to the experts, we derive the adjusted subjective expert weights. The objective weights of experts are derived from the criterion-based entropy method. The ranking of options, we proposed the prospect and regret theory-based TOPSIS ranking approach to rank the options. Finally, a case study on the optimal selection of an AI tool for human-AI collaboration validates the proposed method. Through a comparative analysis with alternative methodologies, we demonstrate the feasibility, stability, and superiority of the proposed model.