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In this paper, we present the formalization and implementation of a decision framework in which a group of agents can express their preferences among a set of alternatives by using several comparison criteria. We define special kind of argument to support agents' preferences, and the set of generated arguments is used both to obtain the set of selected alternatives and to explain why they were selected. We introduce a software application that implements our proposed approach and provides interactive tools for obtaining different kinds of explanations for the computed results. Also, we formally study the proposed argumentation process and we prove that the set of warranted conclusions, which support the obtained results, is consistent. We also show that our framework works properly on choice scenarios with incomplete evidence, and we propose how to optimize the computation process for dynamic environments. [ABSTRACT FROM AUTHOR]

Highlights • Decision support system for applications with multiple comparison criteria. • Users' preferences configurable via conditional rules. • Decision framework based on an argumentative system. • The system provides detailed explanations for the selected alternatives. • Empirical analysis to evaluate the performance of an implemented version. Abstract In our current society, it is very common to have access to a huge amount of information and scenarios in which it is possible to choose among a great number of alternatives. Therefore, systems that automate or assist with the decision-making process are becoming crucial. Those systems should allow users to represent their preferences accurately, even in scenarios where the user has many forms of comparing the available alternatives. In this paper, we propose an argument-based framework for multi-criteria decision-making based on conditional rules, that allows users to encode their preferences among the comparison criteria in terms of requirement and tolerance values. From these rules, arguments that are involved in the comparison process are generated. In order to comply with users' demands for explanations of the given answers, our proposed framework can provide qualitative explanations about the argumentation process, and also an explanatory graph that shows how all the alternatives were ranked and the connections with the user's rules that were used. We also show the results of an empirical analysis of the performance of a developed prototype. [ABSTRACT FROM AUTHOR]

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods. • We propose a framework for defining different types of explanations of AI systems. • We contextualize current XAI discourses within the proposed framework. • We highlight two broad perspectives for defining quality criteria for explainability. • We discuss the relevance of our framework in light of current and upcoming AI regulation. • We confer fundamental aspects for future research of XAI scholars. [ABSTRACT FROM AUTHOR]