More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production
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arXiv
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| Format: | Preprint |
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2026
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| author | Kusters, Amber Prajod, Pooja Cesar, Pablo Ali, Abdallah El |
| author_facet | Kusters, Amber Prajod, Pooja Cesar, Pablo Ali, Abdallah El |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production Kusters, Amber Prajod, Pooja Cesar, Pablo Ali, Abdallah El Human-Computer Interaction H.5.m 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. |
| title | More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production |
| topic | Human-Computer Interaction H.5.m |
| url | https://arxiv.org/abs/2601.11072 |