Fusing domain knowledge with machine learning: A public sector perspective
通过瑞典公共部门两个案例的探索性研究，揭示了将专家领域知识与机器知识（用于预测模型的数据）结合起来的三种机制：巩固、算法中介和自然化，为组织战略性地发展机器学习提供了理论指导。
Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.