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. These methods mainly focus on feature importance and identifying changes that can be made to achieve a desired outcome. Researchers have identified desired properties for XAI methods, such as plausibility, sparsity, causality, low run-time, etc. The objective of this study is to conduct a review of existing XAI research and present a classification of XAI methods. The study also aims to connect XAI users with the appropriate method and relate desired properties to current XAI approaches. The outcome of this study will be a clear strategy that outlines how to choose the right XAI method for a particular goal and user and provide a personalized explanation for users.

翻译：随着人工智能系统日益复杂，可解释人工智能（XAI）领域应运而生，其核心目标是为AI算法的输出提供解释与论证。现有方法主要聚焦于特征重要性识别以及达成预期结果所需调整的确定。研究者已提出XAI方法应具备的理想特性，包括可理解性、稀疏性、因果性、低运行时等。本研究旨在系统梳理现有XAI研究成果，建立XAI方法的分类体系。同时，本研究致力于将XAI用户与适配方法相连接，并将理想特性与当前XAI方法关联。最终成果将形成一套清晰策略，阐明如何针对特定目标与用户选择合适XAI方法，并为用户提供个性化解释。