Understanding User Preferences in Explainable Artificial Intelligence: A Mapping Function Proposal

The increasing complexity of AI systems has led to the growth of the field of Explainable AI (XAI), which aims to provide explanations and justifications for the outputs of AI algorithms. While there is considerable demand for XAI, there remains a scarcity of studies aimed at comprehensively understanding the practical distinctions among different methods and effectively aligning each method with users’ individual needs, and ideally, offer a mapping function which can map each user with its specific needs to a method of explainability. This study endeavors to bridge this gap by conducting a review of the relevant works in XAI, with a specific focus on Explainable Machine Learning (XML), and a keen eye on user needs to provide an observational study. Our main objective is to offer a classification of XAI methods within the realm of XML, categorizing current works into three distinct domains: philosophy, theory, and practice. Moreover, our study seeks to facilitate the connection between XAI users and the most suitable methods for them and tailor explanations to meet their specific needs by proposing a mapping function that take to account users and their desired properties and suggest an XAI method to them. This entails an examination of prevalent XAI approaches and an evaluation of their properties. The primary outcome of this study is the formulation of a clear and concise strategy for selecting the optimal XAI method to achieve a given goal, all while delivering personalized explanations tailored to individual users.