Co-creation with machine learning: Towards a dynamic understanding of knowledge boundaries between developers and end-users

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.