Mitigating Algorithm Aversion through Sensemaking? A Revisit to the Explanation-Seeking Process

Abstract

<p>n this research, we explore the influence of the transition from interpretability-focused to accuracy-driven algorithms on the process of seeking explanations in the context of human-AI collaborations. By extending the perspective of sensemaking, our goal is to shed light on the iterative, reciprocal, and retrospective aspects of the explanationseeking process. Furthermore, we propose the introduction of explanation plausibility as a new contextual factor within the sensemaking framework. To study the long-term explanation-seeking process and trust development in human-AI interactions, we will conduct a qualitative study in the manufacturing sector. Our findings will contribute to the academic discourse on explanation-seeking, sensemaking, and algorithm aversion while offering practical insights for the design of explanation mechanisms and the reduction of algorithm aversion. This research ultimately aims to provide valuable knowledge for both academics and practitioners engaged in information systems, algorithm management, and human-AI collaboration.<br></p>