Abstract
Artificial Intelligence (AI) explainability plays a crucial role in fostering robust Human-AI Interaction (HAI). However, circular reasoning compromises decision robustness due to limitations in existing AI explainability methods. To address this challenge, we propose leveraging human cognition to enhance explainability, aligning with analysis goals without relying on potentially biased labels. By developing text highlighting driven by human gaze patterns, our research demonstrates that human gaze-based text highlighting sig-nificantly reduces decision time for proficient readers, with-out significantly affecting accuracy or bias. This study con-cludes by emphasizing the value of human cognition-based explainability in advancing explainable AI (XAI) and HAI.
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