arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.16627cs.HCcs.AI
收藏

与AI交互：界面设计如何塑造高风险决策中的人机协作

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

University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）

Zichen Chen, Yunhao Luo, Misha Sra

备注：
36 pages, 6 figures, 6 tables. Preprint version
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本研究在糖尿病管理场景中通过108人对照实验评估六种决策支持机制，发现AI置信度、文本解释和性能可视化能提升人机协作表现与信任，而人类反馈和AI提问虽促进反思却因增加认知负担降低表现，强调了平衡设计认知强制功能与可解释AI的重要性。

摘要：

随着对AI系统进行决策的依赖日益增长，确保人类用户能够在AI建议与自身判断之间取得适当平衡变得至关重要，尤其是在医疗保健等高风险领域。然而，研究表明人类+AI团队的表现往往不如单独的AI，有证据表明自动化偏见是导致表现较差的原因，特别是人类往往倾向于遵循AI的建议，即使这些建议是不正确的。在许多现有的人类+AI系统中，决策支持通常以文本解释（XAI）的形式提供，以帮助用户理解AI的推理。由于人类决策往往依赖于系统1思维，用户可能会忽略或未能充分参与这些解释，从而导致糟糕的决策。先前的研究表明，需要新的方法来鼓励用户参与解释，其中一种提出的方法是使用认知强制功能（CFFs）。在这项工作中，我们在糖尿病管理决策场景中考察了各种决策支持机制如何影响用户参与度、信任度以及人机协作任务表现。在一项有108名参与者参与的对照实验中，我们评估了六种决策支持机制的效果，这些机制分为两类解释（文本、视觉）和四种CFFs。我们的发现表明，AI置信度、文本解释和性能可视化等机制提升了人机协作任务的表现，并在提供AI推理线索时改善了信任度。人类反馈和AI驱动的问题等机制鼓励了更深入的反思，但往往通过增加认知努力来降低任务表现，进而影响信任度。像视觉解释这样的简单机制对信任度影响甚微，突显了在CFF和XAI设计中取得平衡的重要性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑