Group decision-making is a daily process where a group of experts has to choose from a set of alternatives. To help this group of experts, methods were developed that allowed them to recommend an alternative or a group of alternatives from the information generated by the experts, these methods were called Group Decision-Making methods. Nonetheless, some Group Decision-Making methods have some problems, such as evaluating alternatives in a general way. This problem leads to a loss of information because it is not possible to evaluate each alternative in detail and it may be the case that one alternative is better than another on one criterion but not on another. Another problem is that experts have priority for each criterion and for one expert one criterion may be more important than another and vice versa. To solve these two problems, in this novel method, we propose a consensual multi-criteria Group Decision-Making system, where experts can customise the order of the criteria. With the order of each expert, the method creates an aggregation operator for each criterion. Moreover, this method performs a consensus analysis for each criterion, so that there is a minimum consensus on each criterion. In this way, a solution is obtained that allows the set of alternatives to be evaluated in a detailed way and each criterion has the correct importance because the experts are the ones who create the order of importance.

Artificial Intelligence (AI) has become increasingly important in critical domains such as medicine, where accurate and interpretable decision-making is essential. However, many high-performing AI models operate as “black boxes”, limiting transparency and making it difficult for clinicians to understand or verify predictions. To address this challenge, we present an eXplainable Artificial Intelligence (XAI) framework that integrates a fuzzy rule-based classifier with genetic algorithms and 2-tuple linguistic representations. The method incrementally generates general fuzzy rules, introduces fuzzy exception rules to capture atypical cases, and applies rule selection and parameter tuning to enhance both accuracy and interpretability. Experiments on nine medical datasets demonstrate that our approach achieves competitive or superior accuracy compared to state-of-the-art algorithms, while requiring fewer rules. These results show that the method not only improves predictive performance but also provides clear, human-readable explanations for each decision, thereby increasing trust and facilitating its application in medical practice.

Measuring agreement among participants in group decision-making problems is critical to such processes. This paper introduces a novel consensus index derived from the Gini coefficient, which avoids the need for traditional aggregation matrices, simplifying calculations while maintaining robustness. The proposed Gini Consensus Index demonstrates properties of reciprocity and boundedness, making it a reliable alternative to traditional distance-based measures. Through a comparative statistical analysis using the Wilcoxon test, the GCI performed similarly to established methods but with computational advantages and enhanced stability. These features make it a promising tool for consensus evaluation in fuzzy preference frameworks.

The aim of this paper is to identify the main factors that determine territorial differences between two large neighbouring provinces (Granada and Almería) that each occupy both sides of the Sierra Nevada mountain range in southern Spain. Our analysis produced a prediction model for riverside woodland cover, one of the variables that most strongly characterizes the differences between the two provinces. A general linear model was constructed for spatial analysis (spherical model with REML estimation), taking into account the location of the municipalities (longitude, latitude, and elevation) and other factors related to soil type, vegetation, or woodland cover. Interpolation of spatial data was performed by ordinary kriging. The predictions obtained over a grid of locations enabled patterns in each region to be derived and visualised, with very clear results. The pattern of riverside woodland cover is highly differentiated between the two provinces, despite their presenting adjoining areas.