Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making

Abstract

As reliance on AI systems for decision-making grows, it becomes critical to ensure that human users can appropriately balance trust in AI suggestions with their own judgment, especially in high-stakes domains like healthcare. However, human + AI teams have been shown to perform worse than AI alone, with evidence indicating automation bias as the reason for poorer performance, particularly because humans tend to follow AI's recommendations even when they are incorrect. In many existing human + AI systems, decision-making support is typically provided in the form of text explanations (XAI) to help users understand the AI's reasoning. Since human decision-making often relies on System 1 thinking, users may ignore or insufficiently engage with the explanations, leading to poor decision-making. Previous research suggests that there is a need for new approaches that encourage users to engage with the explanations and one proposed method is the use of cognitive forcing functions (CFFs). In this work, we examine how various decision-support mechanisms impact user engagement, trust, and human-AI collaborative task performance in a diabetes management decision-making scenario. In a controlled experiment with 108 participants, we evaluated the effects of six decision-support mechanisms split into two categories of explanations (text, visual) and four CFFs. Our findings reveal that mechanisms like AI confidence levels, text explanations, and performance visualizations enhanced human-AI collaborative task performance, and improved trust when AI reasoning clues were provided. Mechanisms like human feedback and AI-driven questions encouraged deeper reflection but often reduced task performance by increasing cognitive effort, which in turn affected trust. Simple mechanisms like visual explanations had little effect on trust, highlighting the importance of striking a balance in CFF and XAI design.