提出全流程可解释AI框架，让各角色都能看懂AI决策全过程。
A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
- 构建涵盖数据到沟通的六阶段解释框架
- 112项问题库覆盖不同用户需求，揭示现有工具短板
- 用大模型生成定制化解释，打通技术与业务理解鸿沟
人工智能正在重塑科学与产业，但许多用户仍视其模型为不可理解的“黑箱”。传统可解释AI方法仅解释单个预测，却忽视上游决策与下游质量验证对可信度的影响。本文提出以用户为中心的全流程可解释人工智能（HXAI）框架，将数据、分析设置、学习过程、模型输出、模型质量与沟通渠道六个环节统一为一个分类体系，并针对领域专家、数据分析师与数据科学家的需求进行适配。基于112项问题库的调研显示当前工具存在显著覆盖空白。该框架融合人类解释理论、人机交互原则与实证研究，提炼出清晰、可操作且认知负荷可控的解释特征。通过系统化分类，减少术语混淆，支持对现有工具链的严谨评估。进一步证明，嵌入大语言模型的AI代理可协调多种解释技术，将技术产物转化为利益相关方专属叙事，弥合开发者与领域专家间的理解差距。本工作突破传统综述局限，整合多学科知识、真实项目经验与文献批判性综述，推动透明、可信与负责任的AI部署新范式。
原文摘要 · Abstract (English)
Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream decisions and downstream quality checks that determine whether insights can be trusted. In this work, we present Holistic Explainable Artificial Intelligence (HXAI), a user-centric framework that embeds explanation into every stage of the data-analysis workflow and tailors those explanations to users. HXAI unifies six components (data, analysis set-up, learning process, model output, model quality, communication channel) into a single taxonomy and aligns each component with the needs of domain experts, data analysts and data scientists. A 112-item question bank covers these needs; our survey of contemporary tools highlights critical coverage gaps. Grounded in theories of human explanation, principles from human-computer interaction and findings from empirical user studies, HXAI identifies the characteristics that make explanations clear, actionable and cognitively manageable. A comprehensive taxonomy operationalises these insights, reducing terminological ambiguity and enabling rigorous coverage analysis of existing toolchains. We further demonstrate how AI agents that embed large-language models can orchestrate diverse explanation techniques, translating technical artifacts into stakeholder-specific narratives that bridge the gap between AI developers and domain experts. Departing from traditional surveys or perspective articles, this work melds concepts from multiple disciplines, lessons from real-world projects and a critical synthesis of the literature to advance a novel, end-to-end viewpoint on transparency, trustworthiness and responsible AI deployment.
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