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

2025-01-28

AI Summary

Addressing systemic risks—including ambiguous copyright attribution, intensified labor control, and eroded editorial sovereignty—arising from commercial large language models (LLMs) in journalism, this study proposes a journalist-led participatory LLM co-design paradigm. Drawing on in-depth qualitative interviews with 20 news professionals across diverse roles, we conducted needs modeling and scenario-based functional specification to develop a controllable, organization-level LLM architecture and core functional specifications tailored to workflows of large, medium, and small news organizations. Our contribution is threefold: (1) introducing the first LLM development framework wherein news organizations serve as governance entities and journalists as principal designers; (2) explicitly safeguarding journalistic intellectual property rights, labor rights, and editorial autonomy; and (3) moving beyond the unidirectional adaptation logic of commercial foundation models, thereby offering both theoretical grounding and actionable pathways for domain-specific, sovereign LLM development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• News Industry
• Language Model
• Copyright Adaptability

Innovation

• Large Language Model Design
• Journalism Application
• User-Centric Adaptation