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

2026-01-16

AI Summary

Current disclosures of AI use in news production often rely on simplistic labels that fail to convey the nuanced complexity of human-AI collaboration. To address this, this study employed a co-design approach to develop four visualization prototypes—textual descriptions, role timelines, task timelines, and chatbot interfaces—and systematically evaluated their impact on user perceptions through an within-subjects experiment integrated with eye-tracking. Findings indicate that the chatbot format conveyed the richest information, whereas textual disclosures were least effective; furthermore, timeline-based visualizations significantly influenced users’ judgments regarding whether AI or humans were the primary agents in the production process. This work contributes actionable visualization strategies and empirical evidence for enhancing transparency in human-AI collaborative systems.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Human-AI collaboration
• disclosure
• journalistic news production
• visualization
• AI transparency

Innovation

• Human-AI collaboration
• disclosure visualization
• journalistic transparency
• co-design
• perception bias