Gradual Voluntary Participation: A Framework for Participatory AI Governance in Journalism

TL;DR

This paper introduces the Gradual Voluntary Participation (GVP) framework for participatory AI governance in journalism, emphasizing a gradual, voluntary approach to stakeholder involvement to enhance trust and legitimacy.

Contribution

The paper proposes the GVP framework, a novel bidimensional model for local participatory AI governance that balances stakeholder influence with organizational dynamics.

Findings

Trust in AI depends on perceived agency within workflows.

GVP addresses epistemic burdens and participatory ceilings.

The framework maps stakeholders across depth and scope dimensions.

Abstract

The integration of AI into journalism challenges participatory design (PD), particularly with respect to stakeholder influence, workplace perceptions, and organizational dynamics. Traditional PD assumes that users can shape technologies, yet AI systems resist influence due to opaque data, fixed architectures, and inaccessible objectives. Through interviews with 10 journalists, we identify the perception gap, showing that trust in AI depends on perceived agency within workplace participatory workflows. Informed by these findings, we introduce the Gradual Voluntary Participation (GVP) framework in journalism and its five core principles, reconceptualizing participation as a gradual and voluntary process that can be operationalized at the newsroom level, beyond fixed workshops or one-time preference-elicitation campaigns. Addressing epistemic burdens, participatory ceilings, and performative consultations, GVP treats gradualism and voluntariness as design dimensions that shape perception, legitimacy, and ownership. Moving beyond unidimensional ladder metaphors and adopting a bidimensional matrix structure, the framework maps stakeholders across depth and scope, offering a new model for local participatory AI governance that balances technological transformation with stakeholder empowerment in rapidly evolving hybrid workplaces.