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

arXiv: https://arxiv.org/abs/2601.11072 · HTML: https://mlsanity.com/paper/2601.11072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 119 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 30 of 31)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [Large Language Models Pass the Turing Test](https://mlsanity.com/paper/2503.23674/) (2025)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence](https://mlsanity.com/paper/2001.02479/) (2020)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [MAILS -- Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies](https://mlsanity.com/paper/2302.09319/) (2023)
- [Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How](https://mlsanity.com/paper/2403.06823/) (2024)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [MiRAGeNews: Multimodal Realistic AI-Generated News Detection](https://mlsanity.com/paper/2410.09045/) (2024)
- ["It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models](https://mlsanity.com/paper/2411.13032/) (2024)
- [Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation](https://mlsanity.com/paper/2502.18357/) (2025)
- [GaussMark: A Practical Approach for Structural Watermarking of Language Models](https://mlsanity.com/paper/2501.13941/) (2025)
- [Envisioning the Applications and Implications of Generative AI for News Media](https://mlsanity.com/paper/2402.18835/) (2024)
- [Secret Use of Large Language Model (LLM)](https://mlsanity.com/paper/2409.19450/) (2024)
- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation](https://mlsanity.com/paper/2404.12056/) (2024)
- [Dark Patterns and the Legal Requirements of Consent Banners: An Interaction Criticism Perspective](https://mlsanity.com/paper/2009.10194/) (2020)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing](https://mlsanity.com/paper/2507.01418/) (2025)
- [Artificial Intelligence & Cooperation](https://mlsanity.com/paper/2012.06034/) (2020)
- [Quality Perceptions and Intended Engagement in Response to AI-Generated and AI-Assisted News](https://mlsanity.com/paper/2409.03500/) (2024)
- [The Future of Work is Blended, Not Hybrid](https://mlsanity.com/paper/2504.13330/) (2025)
- [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025)
- [Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals](https://mlsanity.com/paper/2505.16057/) (2025)

## Built on by (most cited first, 3 of 3)

- [Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News](https://mlsanity.com/paper/2605.14999/) (2026)
- [Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News](https://mlsanity.com/paper/2606.11116/) (2026)
- [Runtime Action Interference for AI Control of AlphaStar in StarCraft II](https://mlsanity.com/paper/2608.21398/) (2026)

## Abstract (arXiv)

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.