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.

翻译：可解释人工智能（XAI）旨在使机器学习模型透明且可信，然而当前大多数方法通过视觉或文本形式传达解释。本文引入一个信息论框架，用于分析解释模态——特别是语音与文本——如何影响用户对人工智能系统的理解与信任校准。所提出的模型将解释传递视为模型与用户之间的通信信道，并通过信息保留度、理解效率（CE）与信任校准误差（TCE）等指标进行表征。研究开发了一个基于Python实现的仿真框架，通过使用基于合成SHAP的特征归因，在多种模态风格配置（简洁型、详细型与类比型）下评估这些指标。结果表明，文本解释能实现更高的理解效率，而语音解释则带来更优的信任校准效果，其中基于类比的传递方式实现了最佳的整体权衡。该框架为设计与评估多模态可解释性系统提供了可复现的基础，并可扩展至基于真实SHAP或LIME输出在开放数据集（如UCI信用审批或Kaggle金融交易数据集）上的实证研究。