发表机构
Renmin University of China（中国人民大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将数据叙事与可解释机器学习结合，通过“What-if”和“Why-not”故事及数据脱敏，为非专家解读AI决策，实证显示可及性显著优于传统SHAP可视化。
AI 中文摘要
AI驱动的自动化决策既需要预测性能，也需要可解释性。可解释机器学习（IML）的最新进展为解释模型预测提供了工具，但这些解释的技术复杂性可能阻碍非专家对其的可及性。为应对这一挑战，本研究将数据叙事与IML相结合，以增强AI生成决策对更广泛受众的可解释性。遵循设计科学研究（DSR）范式，本研究提出了IML中数据叙事的正式定义，引入了DIST金字塔以将数据叙事与IML对齐，并提出了I-P-O模型来描述它们的交互。研究进一步开发了一种架构，通过独特的“What-if”和“Why-not”事件生成过程来解释AI决策。该架构还采用数据脱敏来保护敏感输入数据。为验证该方法，使用波士顿住房数据集进行了一项案例研究，利用SHapley Additive exPlanations（SHAP）值和大型语言模型（LLMs）生成具有And-But-Therefore（ABT）结构的数据故事。一项实证评估显示，76.4%和74.3%的受访者分别认为“What-if”和“Why-not”数据故事更易理解，其可及性得分显著高于传统的SHAP可视化。论文最后提出了一个整合IML和数据叙事的叙事解释框架，从而扩展了AI决策的研究范围和实践适用性。
英文摘要
AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) provide tools for explaining model predictions, but the technical complexity of these explanations may hinder accessibility to non-experts. To address this challenge, this study integrates data storytelling with IML to enhance the explainability of AI-generated decisions for a broader audience. Following the design science research (DSR) paradigm, this study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions. It further develops an architecture to explain AI decisions through distinct "What-if" and "Why-not" event-generation processes. The architecture also employs data desensitization to protect sensitive input data. To validate the approach, a case study is conducted with the Boston Housing dataset, using SHapley Additive exPlanations (SHAP) values and large language models (LLMs) to generate data stories with And-But-Therefore (ABT) structures. An empirical evaluation shows that 76.4% and 74.3% of respondents rated the "What-if" and "Why-not" data stories as more comprehensible, with significantly higher accessibility scores than traditional SHAP visualizations. The paper concludes with the presentation of a narrative interpretation framework that integrates IML and data storytelling, thereby expanding the research scope as well as the practical applicability of AI decision-making.
