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

Researchers developed and tested visual disclosure methods for communicating human-AI collaboration in journalism, finding that simple text labels fail to convey nuance while interactive formats like chatbot interfaces provide more transparency. The study reveals that visualization design significantly influences reader perception of AI's actual role in news production, raising concerns about how disclosure formats can misrepresent collaborative contribution ratios.

This research addresses a critical gap in AI transparency within media organizations. As newsrooms increasingly integrate AI tools for research, writing, and editing, disclosure practices remain primitive—typically limited to generic labels that obscure the complexity of human-AI workflows. The researchers conducted rigorous evaluation of multiple visualization approaches, discovering that traditional textual disclosures are ineffective at communicating collaborative dynamics, while timeline-based and interactive formats enable readers to understand specific contributions.

The findings expose a concerning pattern: visualization design doesn't merely inform readers neutrally but actively shapes perception of AI's role. Role-based timelines amplified perceived AI contribution in articles primarily authored by humans, while task-based timelines shifted perception toward human involvement in AI-heavy pieces. This perceptual manipulation occurs regardless of actual collaboration ratios, suggesting that disclosure format functions as an editorial tool that can obscure rather than illuminate.

For media organizations, content platforms, and regulators, this research establishes baseline standards for AI disclosure in journalism. Current practices fail stakeholder accountability requirements and consumer trust. The study demonstrates that transparency requires sophisticated design thinking, not minimal compliance labeling. This becomes increasingly important as generative AI adoption accelerates across newsrooms globally.

The research signals growing maturity in AI ethics scholarship by moving beyond binary "use it or don't" framings toward nuanced examination of implementation practices. Future journalism standards will likely incorporate visualization-based disclosure requirements, potentially influenced by these findings.

• →Simple text disclosures of AI usage in journalism are ineffective; visual formats significantly outperform traditional labels in communicating human-AI collaboration.
• →Disclosure visualization design actively shapes reader perception of AI contribution, sometimes misrepresenting actual involvement ratios regardless of accuracy.
• →Interactive chatbot-style disclosures provide the most detailed information about collaboration processes compared to timeline or textual alternatives.
• →Role-based and task-based timelines produce opposite perceptual biases, amplifying or minimizing AI contributions depending on presentation structure.
• →Standardized disclosure practices for AI in journalism require sophisticated design consideration rather than minimal compliance labeling.