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

Emily Tseng, Meg Young, Marianne Aubin Le Quéré, Aimee Rinehart, Harini Suresh

TL;DR

The paper investigates how journalism can shape and govern large language models through a participatory, journalist-led design process. Using participatory action research, semi-structured interviews, and design fiction with 20 newsroom stakeholders, it identifies macro, meso, and micro tensions that influence AI adoption in newsrooms. It then proposes the Newsroom Tooling Alliance (NTA), a journalist-led cooperative that pools data under a governance framework, fine-tunes an open-source LLM, and conducts ongoing audits to ensure ethical use and revenue-sharing, while remaining non-exclusive. The work argues that commercial foundation models are ill-suited for newsroom needs and demonstrates how participatory design can yield more trustworthy, controllable, and capacity-building AI tooling for journalism, with implications for practice and methodology in AI governance.

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.

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

TL;DR

The paper investigates how journalism can shape and govern large language models through a participatory, journalist-led design process. Using participatory action research, semi-structured interviews, and design fiction with 20 newsroom stakeholders, it identifies macro, meso, and micro tensions that influence AI adoption in newsrooms. It then proposes the Newsroom Tooling Alliance (NTA), a journalist-led cooperative that pools data under a governance framework, fine-tunes an open-source LLM, and conducts ongoing audits to ensure ethical use and revenue-sharing, while remaining non-exclusive. The work argues that commercial foundation models are ill-suited for newsroom needs and demonstrates how participatory design can yield more trustworthy, controllable, and capacity-building AI tooling for journalism, with implications for practice and methodology in AI governance.

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.

Paper Structure

This paper contains 32 sections, 2 tables.