Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as the extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential.

学习算法，产生响应，分类或动态预测的技术，类似于知识工作者的响应，为组织学者提出了与工作和组织有关的重要研究问题。我们建议，此类算法应从以下四个方面加以区分：黑盒性能，全面数字化，预期量化和隐藏政治。这些方面很可能会改变工作方式和组织，从本质上讲，不仅仅是简单地暗示长期技术趋势的加速。我们的分析表明，学习算法将改变组织的专业知识，重塑工作和职业界限，并提供新颖的协调和控制形式。从而，由于学习算法的使用可以在一定程度上影响并改变工作和组织的现实状况，因此可以认为它们是有效的。它们的快速部署要求学术界关注社会问题，例如授权该算法做出决策的程度，将道德纳入技术的需求以及其数字化铁笼潜力。