比较语音与文字解释对用户理解与信任的影响
An Information-Theoretic Framework for Comparing Voice and Text Explainability
- 用信息论框架分析语音和文字解释的传播效率
- 文字解释理解更高效，语音解释信任更准确
- 比喻式表达在两者间取得最佳平衡，适合实际应用
可解释人工智能（XAI）旨在提升机器学习模型的透明度与可信度，但现有方法多以视觉或文字形式传达解释。本文提出一种信息论框架，分析解释模态（语音与文字）如何影响用户对AI系统的理解与信任校准。该模型将解释传递视为模型与用户间的通信信道，引入信息保留、理解效率（CE）与信任校准误差（TCE）等指标进行评估。基于Python构建的仿真框架，使用合成的SHAP特征归因，在多种模态风格配置（简短、详细、比喻式）下进行测试。结果表明：文字解释具有更高理解效率，语音解释实现更好信任校准，而比喻式表达在整体表现上达到最优平衡。该框架为多模态可解释系统的设计与评测提供可复现基础，并可扩展至使用真实SHAP或LIME输出的实证研究，如UCI信用审批或Kaggle金融交易数据集。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence (XAI) aims to make machine learning models transparent and trustworthy, yet most current approaches communicate explanations visually or through text. This paper introduces an information theoretic framework for analyzing how explanation modality specifically, voice versus text affects user comprehension and trust calibration in AI systems. The proposed model treats explanation delivery as a communication channel between model and user, characterized by metrics for information retention, comprehension efficiency (CE), and trust calibration error (T CE). A simulation framework implemented in Python was developed to evaluate these metrics using synthetic SHAP based feature attributions across multiple modality style configurations (brief, detailed, and analogy based). Results demonstrate that text explanations achieve higher comprehension efficiency, while voice explanations yield improved trust calibration, with analogy based delivery achieving the best overall trade off. This framework provides a reproducible foundation for designing and benchmarking multimodal explainability systems and can be extended to empirical studies using real SHAP or LIME outputs on open datasets such as the UCI Credit Approval or Kaggle Financial Transactions datasets.
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