用可视化设计揭示新闻中人机协作细节，避免标签化误导读者。
More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production
- 设计四类视觉化披露方案，展示人机协作流程与角色分工。
- 聊天机器人型披露最全面，文本型效果最差，影响读者认知判断。
- 时间线类型可引导观众对人机角色的感知，需警惕误导风险。
在新闻编辑过程中，当前对AI使用的披露仅限于简单的标签，无法体现人机协作的真实细节。通过10次共同设计工作坊，我们收集了69种披露设计方案，并实现了四种原型，用于视觉化呈现新闻生产中的人机协作。随后开展一项包含32名参与者的组内实验，考察不同披露形式（文本、基于角色的时间线、基于任务的时间线、聊天机器人）及协作比例（主要由人完成或主要由AI完成）如何影响用户对可视化信息的理解、注视模式及体验反馈。结果显示，文本披露最不有效，而聊天机器人形式提供最深入的信息；基于角色的时间线会放大主要由人完成的文章中AI的作用，而基于任务的时间线则在主要由AI完成的文章中增强了人类参与的感知。本研究贡献了人机协作披露的可视化设计及其评估，提醒我们视觉呈现可能扭曲公众对AI实际角色的认知。
原文摘要 · Abstract (English)
Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article. Through co-design sessions (N=10), we elicited 69 disclosure designs and implemented four prototypes that visually disclose human-AI collaboration in journalism. We then ran a within-subjects lab study (N=32) to examine how disclosure visualizations (Textual, Role-based Timeline, Task-based Timeline, Chatbot) and collaboration ratios (Primarily Human vs. Primarily AI) influenced visualization perceptions, gaze patterns, and post-experience responses. We found that textual disclosures were least effective in communicating human-AI collaboration, whereas Chatbot offered the most in-depth information. Furthermore, while role-based timelines amplified AI contribution in primarily human articles, task-based timeline shifted perceptions toward human involvement in primarily AI articles. We contribute Human-AI collaboration disclosure visualizations and their evaluation, and cautionary considerations on how visualizations can alter perceptions of AI's actual role during news article creation.
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