理解可解释人工智能中的用户偏好：综述与映射函数提案
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal
AI总结：
本研究针对XAI领域缺乏用户需求与方法匹配研究的问题，通过综述XML范畴的XAI研究并按哲学、理论、实践分类，提出考量用户属性的映射函数，实现最优XAI方法的个性化选择。
摘要：
AI系统复杂度的不断提升推动了可解释人工智能（Explainable Artificial Intelligence, XAI）领域的发展，该领域旨在为AI算法的输出提供解释与合理性证明。尽管XAI的需求十分旺盛，但目前仍缺乏旨在全面理解不同方法间实际差异、并有效将各类方法与用户个性化需求匹配的研究，理想情况下这类研究还应提供一个映射函数，可将有特定需求的每位用户对应到一种可解释性方法。本研究致力于填补这一空白，我们对XAI领域的现有研究开展了全面综述，重点聚焦可解释机器学习（Explainable Machine Learning, XML），并密切关注用户需求。我们的核心目标是对XML范畴内的XAI方法进行分类，将现有研究划分为三个不同领域：哲学、理论与实践，并对每个类别展开批判性综述。此外，本研究旨在通过提出一种映射函数，推动XAI用户与最适合自身的方法建立关联，并定制符合其特定需求的解释；该函数会考量用户及其期望的属性，为其推荐一种XAI方法。这一过程涉及对主流XAI方法的梳理及其属性的评估。本研究的主要成果是制定了一套清晰简洁的策略，用于选择实现既定目标的最优XAI方法，同时为不同用户提供量身定制的个性化解释。
英文摘要：
The increasing complexity of AI systems has led to the growth of the field of Explainable Artificial Intelligence (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 thorough review of extant research in XAI, with a specific focus on Explainable Machine Learning (XML), and a keen eye on user needs. 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, and providing a critical review for each category. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
