The introduction of machine learning (ML)in organizations comes with the claim that algorithms will produce insights superior to those of experts by discovering the “truth” from data. Such a claim gives rise to a tension between the need to produce knowledge independent of domain experts and the need to remain relevant to the domain the system serves. This two-year ethnographic study focuses on how developers managed this tension when building an ML system to support the process of hiring job candidates at a large international organization. Despite the initial goal of getting domain experts “out the loop,” we found that developers and experts arrived at a new hybrid practice that relied on a combination of ML and domain expertise. We explain this outcome as resulting from a process of mutual learning in which deep engagement with the technology triggered actors to reflect on how they produced knowledge. These reflections prompted the developers to iterate between excluding domain expertise from the ML system and including it. Contrary to common views that imply an opposition between ML and domain expertise, our study foregrounds their interdependence and as such shows the dialectic nature of developing ML. We discuss the theoretical implications of these findings for the literature on information technologies and knowledge work, information system development and implementation, and human–ML hybrids.

中文翻译：

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当机器遇到专家时：开发人工智能以供招聘的民族志

在组织中引入机器学习 (ML) 的同时声称，算法将通过从数据中发现“真相”来产生优于专家的洞察力。这样的主张在需要产生独立于领域专家的知识与需要保持与系统所服务的领域相关的需要之间产生了张力。这项为期两年的民族志研究重点关注开发人员在构建 ML 系统以支持大型国际组织招聘求职者的过程时如何应对这种紧张局势。尽管最初的目标是让领域专家“走出循环”，但我们发现开发人员和专家达成了一种新的混合实践，该实践依赖于机器学习和领域专业知识的结合。我们将这一结果解释为相互学习过程的结果，在该过程中，对技术的深入参与促使参与者反思他们是如何产生知识的。这些反思促使开发人员在从 ML 系统中排除领域专业知识和将其包括在内之间进行迭代。与暗示 ML 和领域专业知识之间对立的常见观点相反，我们的研究突出了它们的相互依存性，因此显示了发展 ML 的辩证性质。我们讨论了这些发现对信息技术和知识工作、信息系统开发和实施以及人类-机器学习混合的文献的理论意义。这些反思促使开发人员在从 ML 系统中排除领域专业知识和将其包括在内之间进行迭代。与暗示 ML 和领域专业知识之间对立的常见观点相反，我们的研究突出了它们的相互依存性，因此显示了发展 ML 的辩证性质。我们讨论了这些发现对信息技术和知识工作、信息系统开发和实施以及人类-机器学习混合的文献的理论意义。这些反思促使开发人员在从 ML 系统中排除领域专业知识和将其包括在内之间进行迭代。与暗示 ML 和领域专业知识之间对立的常见观点相反，我们的研究突出了它们的相互依存性，因此显示了发展 ML 的辩证性质。我们讨论了这些发现对信息技术和知识工作、信息系统开发和实施以及人类-机器学习混合的文献的理论意义。