Machine learning tools are increasingly infiltrating everyday work life with implications for workers. By looking at machine learning tools as part of a sociotechnical system, we explore how machine learning tools enforce oppression of workers. We theorize, normatively, that with reorganizing processes in place, oppressive characteristics could be converted to emancipatory characteristics. Drawing on Paulo Freire’s critical theory of emancipatory pedagogy, we outline similarities between the characteristics Freire saw in oppressive societies and the characteristics of currently designed partnerships between humans and machine learning tools. Freire’s theory offers a way forward in reorganizing humans and machine learning tools in the workplace. Rather than advocating human control or the decoupling of workers and machines, we follow Freire’s theory in proposing four processes for emancipatory organizing of human and machine learning partnership: 1) awakening of a critical consciousness, 2) enabling role freedom, 3) instituting incentives and sanctions for accountability, and 4) identifying alternative emancipatory futures. Theoretical and practical implications of this emancipatory organizing theory are drawn.

机器学习工具正日益渗透到日常生活中，这对工人产生了影响。通过将机器学习工具视为一种社会技术系统的一部分，我们探索了机器学习工具如何增强对工人的压迫。我们理论上认为，通过适当的重组过程，压迫性特征可以转化为解放性特征。借鉴Paulo Freire的解放教育学批判理论，我们概述了Freire在压迫性社会中所看到的特征与当前设计的人与机器学习工具之间的伙伴关系之间的相似性。弗雷雷（Freire）的理论为在工作场所重组人员和机器学习工具提供了前进的道路。与其提倡人为控制或使工人与机器脱钩，不如说是，我们遵循弗雷雷（Freire）的理论，提出了人类和机器学习伙伴关系的解放组织的四个过程：1）唤醒批判意识，2）赋予角色自由，3）设立激励和制裁责任制，以及4）确定替代的解放性未来。提出了这种解放组织理论的理论和实践意义。