Human Cognition for Mitigating the Paradox of AI Explainability: A Pilot Study on Human Gaze-based Text Highlighting

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 significantly reduces decision time for proficient readers, without significantly affecting accuracy or bias. This study concludes by emphasizing the value of human cognition-based explainability in advancing explainable AI (XAI) and HAI.