The impact of machine learning within public organizations relies on coordinated effort over the functional chain from data generation to decision-making. This coordination faces challenges due to the separation between data intelligence departments and operational intelligence. Through theory about knowledge sharing between occupational communities and a case study at a Dutch inspectorate, we explore knowledge boundaries between machine learning developers and end-users and the effects of co-creation. Our analysis reveals that knowledge boundaries are dynamic, with boundaries blurring, persisting, and emerging under the influence of co-creation. Especially the emergence of boundaries is surprising and suggests the presence of a waterbed effect. Furthermore, knowledge boundaries are layered phenomena, with some boundary types more prone to change than others. Understanding knowledge boundaries and their dynamics better can be crucial for improving the intended impact of ML for organizations.

中文翻译：

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与机器学习共创：实现对开发人员和最终用户之间知识边界的动态理解


机器学习在公共组织内的影响取决于从数据生成到决策的功能链上的协调工作。由于数据智能部门和运营智能之间的分离，这种协调面临挑战。通过关于职业社区之间知识共享的理论和荷兰检查局的案例研究，我们探讨了机器学习开发人员和最终用户之间的知识边界以及共同创造的影响。我们的分析表明，知识边界是动态的，在共同创造的影响下，边界模糊、持续和出现。特别是边界的出现令人惊讶，并表明存在水床效应。此外，知识边界是分层现象，某些边界类型比其他边界类型更容易发生变化。更好地了解知识边界及其动态对于改善 ML 对组织的预期影响至关重要。