# "Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism

arXiv: https://arxiv.org/abs/2501.17299 · HTML: https://mlsanity.com/paper/2501.17299/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 66 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [ReelFramer: Human-AI Co-Creation for News-to-Video Translation](https://mlsanity.com/paper/2304.09653/) (2023)
- [A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research](https://mlsanity.com/paper/2207.04171/) (2022)
- [Envisioning the Applications and Implications of Generative AI for News Media](https://mlsanity.com/paper/2402.18835/) (2024)
- [Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism](https://mlsanity.com/paper/2311.06864/) (2023)
- [Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment](https://mlsanity.com/paper/2310.06361/) (2023)
- ["It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry](https://mlsanity.com/paper/2409.12000/) (2024)
- [Towards Designing a Question-Answering Chatbot for Online News: Understanding Questions and Perspectives](https://mlsanity.com/paper/2312.10650/) (2023)
- [The Impact of Knowledge Silos on Responsible AI Practices in Journalism](https://mlsanity.com/paper/2410.01138/) (2024)
- [Scrolly2Reel: Retargeting Graphics for Social Media Using Narrative Beats](https://mlsanity.com/paper/2403.18111/) (2024)

## Built on by (most cited first, 10 of 10)

- [When Style Breaks Safety: Defending LLMs Against Superficial Style Alignment](https://mlsanity.com/paper/2506.07452/) (2025)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Towards Real-World Validity in Generative AI Benchmarks: Understanding and Designing Domain-Centered Evaluations for Journalism Practitioners](https://mlsanity.com/paper/2511.05501/) (2025)
- [Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies](https://mlsanity.com/paper/2602.08187/) (2026)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- ["Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community](https://mlsanity.com/paper/2604.16224/) (2026)
- [Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation](https://mlsanity.com/paper/2606.03761/) (2026)
- [Editorial Alignment: A Participatory Approach to Engaging Editorial Expertise in LLM-mediated Knowledge Dissemination](https://mlsanity.com/paper/2606.20258/) (2026)

## Abstract (arXiv)

Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already permeate newswork processes within financially constrained organizations, even as ongoing legal challenges assert that AI companies violate their copyright. At stake are key questions about what LLMs are created to do, and by whom: How might a journalist-led LLM work, and what can participatory design illuminate about the present-day challenges about adapting ``one-size-fits-all'' foundation models to a given context of use? In this paper, we undertake a co-design exploration to understand how a participatory approach to LLMs might address opportunities and challenges around AI in journalism. Our 20 interviews with reporters, data journalists, editors, labor organizers, product leads, and executives highlight macro, meso, and micro tensions that designing for this opportunity space must address. From these desiderata, we describe the result of our co-design work: organizational structures and functionality for a journalist-controlled LLM. In closing, we discuss the limitations of commercial foundation models for workplace use, and the methodological implications of applying participatory methods to LLM co-design.