ABSTRACT

In this paper, we employ John Dewey’s notion of the situation as an analytic lens for observing and theorizing machine learning. Based on two ethnographic case studies in art and science, we account for machine learning as practice and examine the dynamics of the situations it gives rise to. Following Dewey, our observations focus on the transformation of situations from an initial state of indeterminacy through to problematizations and their resolution. Rethinking machine learning through the situation, we analyze how cooperating machine learners, both human and non-human, resolve situations and thereby refine their mutual attunement. With Dewey, we first explain how machine learners train through disruption and adaptation as they identify and solve problems. Second, we show that these problems concern issues of latency and addressability in efforts of cooperation between heterogeneous machine learners. Third, we discuss how machine learning practices cultivate situations that feature careful calibrations of problems that allow for their productive transformation. Our empirically grounded approach offers a pragmatist account of machine learning as a continually indeterminate and dynamic situated practice. As a contribution to ongoing discussions in social theory, we reframe existing characterizations of machine learning as issues of latency and addressability in cooperation.

摘要

在本文中，我们采用约翰·杜威的情境概念作为观察和理论化机器学习的分析镜头。基于艺术和科学领域的两个人种学案例研究，我们将机器学习视为实践，并研究它所引发的情况的动态。继杜威之后，我们的观察重点是情况从最初的不确定状态到问题化及其解决的转变。通过情境重新思考机器学习，我们分析了人类和非人类的合作机器学习者如何解决情境，从而完善它们的相互协调。通过杜威，我们首先解释机器学习者在识别和解决问题时如何通过干扰和适应进行训练。第二，我们表明，这些问题涉及异构机器学习者之间合作的延迟和可寻址性问题。第三，我们讨论机器学习实践如何培养对问题进行仔细校准的情况，从而实现富有成效的转变。我们以经验为基础的方法提供了一种实用主义的解释，将机器学习视为一种持续不确定且动态的实践。作为对社会理论中正在进行的讨论的贡献，我们将机器学习的现有特征重新定义为合作中的延迟和可寻址性问题。我们以经验为基础的方法提供了一种实用主义的解释，将机器学习视为一种持续不确定且动态的实践。作为对社会理论中正在进行的讨论的贡献，我们将机器学习的现有特征重新定义为合作中的延迟和可寻址性问题。我们以经验为基础的方法提供了一种实用主义的解释，将机器学习视为一种持续不确定且动态的实践。作为对社会理论中正在进行的讨论的贡献，我们将机器学习的现有特征重新定义为合作中的延迟和可寻址性问题。