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.

中文翻译：

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将领域知识与机器学习融合：公共部门的视角


机器学习 (ML) 为公共和私营部门组织提供了广泛认可但复杂的机会，以从数据中创造价值。一个关键要求是，组织必须找到开发新知识的方法，将相关领域专家的关键“领域知识”与“机器知识”（即可用于为预测模型提供信息的数据）相结合。在本文中，我们认为理解生成此类知识的过程对于战略性地开发机器学习至关重要。为了促进这种理解，我们通过对瑞典公共部门的两个案例进行探索性研究，研究了通过机器学习从领域知识生成新知识的过程。研究结果揭示了三种机制（称为整合、算法中介和自然化）在将领域知识与机器知识联系起来方面的作用。该研究提出了与机器学习的组织使用相关的知识生产理论，对其战略治理（尤其是公共部门）具有重要影响。