Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration
Proceedings of the 29th International Conference on Intelligent User Interfaces(2024)
摘要
Although recent developments in generative AI have greatly enhanced the
capabilities of conversational agents such as Google's Bard or OpenAI's
ChatGPT, it's unclear whether the usage of these agents aids users across
various contexts. To better understand how access to conversational AI affects
productivity and trust, we conducted a mixed-methods, task-based user study,
observing 76 software engineers (N=76) as they completed a programming exam
with and without access to Bard. Effects on performance, efficiency,
satisfaction, and trust vary depending on user expertise, question type
(open-ended "solve" questions vs. definitive "search" questions), and
measurement type (demonstrated vs. self-reported). Our findings include
evidence of automation complacency, increased reliance on the AI over the
course of the task, and increased performance for novices on "solve"-type
questions when using the AI. We discuss common behaviors, design
recommendations, and impact considerations to improve collaborations with
conversational AI.
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