An Information-Theoretic Framework for Comparing Voice and Text Explainability

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 (TCE). 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.