Loading...

Media is loading
 

Paper Number

2819

Paper Type

LitReview

Abstract

The widespread integration of AI systems across diverse areas of society and the economy offers considerable potential to assist humans in decision-making processes. However, as these systems often exhibit a high degree of complexity and act like black boxes in their decision-making, the evolving research field of Explainable AI (XAI) attempts to make the internal functioning and decision-making mechanisms of AI systems comprehensible to humans. This raises the question of how such XAI methods are perceived by humans. To gain a comprehensive understanding of user perceptions in human-XAI interaction, we conduct a systematic literature review. Our findings reveal five categories of user perception, which are influenced by multiple factors and result in behavioral and performance outcomes. This study contributes to a holistic understanding of user perceptions in the context of Explainable AI, benefiting both theoretical and practical aspects of information systems and XAI research.

Comments

25-LitReviews

Download

Share

COinS
 
Dec 15th, 12:00 AM

In the Eyes of the User: A Systematic Literature Review of User Perceptions in Human-XAI Interaction

The widespread integration of AI systems across diverse areas of society and the economy offers considerable potential to assist humans in decision-making processes. However, as these systems often exhibit a high degree of complexity and act like black boxes in their decision-making, the evolving research field of Explainable AI (XAI) attempts to make the internal functioning and decision-making mechanisms of AI systems comprehensible to humans. This raises the question of how such XAI methods are perceived by humans. To gain a comprehensive understanding of user perceptions in human-XAI interaction, we conduct a systematic literature review. Our findings reveal five categories of user perception, which are influenced by multiple factors and result in behavioral and performance outcomes. This study contributes to a holistic understanding of user perceptions in the context of Explainable AI, benefiting both theoretical and practical aspects of information systems and XAI research.

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.