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.

翻译：人工智能与新闻业的融合对参与式设计（PD）提出了挑战，特别是在利益相关者影响力、工作场所认知及组织动态方面。传统参与式设计假设用户能够塑造技术，然而人工智能系统由于不透明的数据、固定的架构及难以触及的目标而难以被影响。通过对10名记者的访谈，我们识别出认知差距，表明对人工智能的信任取决于在工作场所参与式流程中所感知到的能动性。基于这些发现，我们提出了新闻业中的渐进式自愿参与（GVP）框架及其五项核心原则，将参与重新概念化为一个可在新闻编辑室层面操作的渐进、自愿过程，超越了固定的研讨会或一次性偏好征集活动。为解决认知负担、参与上限及表演性咨询等问题，GVP将渐进性与自愿性视为塑造认知、合法性与所有权的设计维度。该框架摒弃了单一维度的阶梯隐喻，采用双维矩阵结构，将利益相关者按深度与广度进行映射，为本地化参与式人工智能治理提供了新模型，在快速演变的混合型工作场所中平衡技术转型与利益相关者赋权。