Explainable AI (XAI) is often criticized for failing to satisfy broad desiderata (e.g., fairness, accountability) and for limited practical value to stakeholders. This challenge partly arises because researchers across disciplines prioritize different sets of desiderata that remain underspecified and context-dependent, yet expect XAI to satisfy them simultaneously, resulting in fragmented and sometimes incompatible operationalizations. We argue that many desiderata are not independent, but instead form dependency structures in which higher-level goals (\emph{e.g.}, trust, accountability) rely on more foundational properties (\emph{e.g.}, faithfulness, robustness). Some desiderata are multi-faceted and are best understood within these structures. In particular, instead of addressing all desiderata at once, we focus on subsets of dependency structures and translate them into concrete XAI tasks, thereby decomposing research questions into benchmarkable and solvable units. To this end, we propose a three-axis taxonomy (\emph{target}, \emph{functional role}, and \emph{mode of justification}) and a three-step framework for deriving well-scoped, benchmarkable XAI tasks. Our approach builds on a systematic literature review and conceptual analysis, and supports clarifying desiderata, identifying dependencies, scoping feasibility, and delimiting the design space to derive concrete XAI tasks from abstract desiderata. We illustrate its utility through two explanatory cases, showing how the taxonomy and framework guide systematic task design and evaluation in XAI. {\color{red}{This is a preprint of a paper that will appear in AISoLA 2026.}}

翻译：可解释人工智能（XAI）常因未能满足广泛的期求（如公平性、问责性）以及对利益相关者的实际价值有限而受到批评。这一挑战部分源于不同学科的研究者优先考虑不同的期求集，这些期求往往定义不清且依赖上下文，却又期望XAI同时满足它们，导致碎片化甚至不相容的操作化实现。我们认为，许多期求并非相互独立，而是构成依赖结构：高层次目标（如信任、问责性）依赖于更基础的属性（如忠实性、鲁棒性）。部分期求具有多面性，只有在此类结构中才能得到最佳理解。特别地，我们并非同时处理所有期求，而是聚焦于依赖结构的子集，并将其转化为具体的XAI任务，从而将研究问题分解为可基准化、可解决的单元。为此，我们提出一个三轴分类法（目标、功能角色与论证模式）和一个三步框架，用于推导范围明确、可基准化的XAI任务。我们的方法基于系统性文献综述与概念分析，有助于厘清期求、识别依赖关系、界定可行性范围并限定设计空间，从而从抽象期求中推导出具体的XAI任务。我们通过两个解释性案例展示了其效用，说明该分类法与框架如何指导XAI中系统性任务设计与评估。{\color{red}{本文为预印本，将发表于AISoLA 2026。}}