Emotion-sensitive Explanation Model

TL;DR

This paper introduces a three-stage model for emotion-sensitive explanations in AI, aiming to adapt explanations based on users' emotional states to enhance understanding and decision-making.

Contribution

It proposes a novel three-stage model for emotion-sensitive explanation grounding, integrating emotional factors into XAI to improve user-centered explanations.

Findings

Model provides a conceptual basis for emotion-adaptive explanations

Highlights importance of emotional states in explanation understanding

Supports development of more effective, user-centered XAI systems

Abstract

Explainable AI (XAI) research has traditionally focused on rational users, aiming to improve understanding and reduce cognitive biases. However, emotional factors play a critical role in how explanations are perceived and processed. Prior work shows that prior and task-generated emotions can negatively impact the understanding of explanation. Building on these insights, we propose a three-stage model for emotion-sensitive explanation grounding: (1) emotional or epistemic arousal, (2) understanding, and (3) agreement. This model provides a conceptual basis for developing XAI systems that dynamically adapt explanation strategies to users emotional states, ultimately supporting more effective and user-centered decision-making.