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More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production

Published: January 16, 2026 | arXiv ID: 2601.11072v1

By: Amber Kusters , Pooja Prajod , Pablo Cesar and more

Potential Business Impact:

Shows how people and AI wrote news together.

Business Areas:
Human Computer Interaction Design, Science and Engineering

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.

Page Count
33 pages

Category
Computer Science:
Human-Computer Interaction