Machine Learning (ML) systems, particularly when deployed in high-stakes domains, are deeply consequential. They can exacerbate existing inequities, create new modes of discrimination, and reify outdated social constructs. Accordingly, the social context (i.e. organisations, teams, cultures) in which ML systems are developed is a site of active research for the field of AI ethics, and intervention for policymakers. This paper focuses on one aspect of social context that is often overlooked: interactions between practitioners and the tools they rely on, and the role these interactions play in shaping ML practices and the development of ML systems. In particular, through an empirical study of questions asked on the Stack Exchange forums, the use of interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML practices is explored. I find that interactive computing platforms are used in a host of learning and coordination practices, which constitutes an infrastructural relationship between interactive computing platforms and ML practitioners. I describe how ML practices are co-evolving alongside the development of interactive computing platforms, and highlight how this risks making invisible aspects of the ML life cycle that AI ethics researchers' have demonstrated to be particularly salient for the societal impact of deployed ML systems.

翻译：机器学习系统，尤其是在高风险领域部署时，具有深远影响。它们可能加剧现有不平等、创造新的歧视模式，并固化过时的社会建构。因此，开发机器学习系统的社会情境（即组织、团队、文化）已成为人工智能伦理领域的活跃研究对象，也是政策制定者的干预重点。本文聚焦于社会情境中常被忽视的一个方面：实践者与其依赖工具之间的互动，以及这些互动在塑造机器学习实践与系统开发过程中所起的作用。具体而言，通过对Stack Exchange论坛上提出问题的实证研究，探讨了交互式计算平台（如Jupyter Notebook和Google Colab）在机器学习实践中的应用。我发现，交互式计算平台被用于一系列学习与协作实践中，这表明交互式计算平台与机器学习实践者之间存在基础设施层面的关系。我描述了机器学习实践如何与交互式计算平台的发展共同演进，并强调这种演进如何可能导致机器学习生命周期中某些方面的隐性化——而人工智能伦理研究者已证明这些方面对已部署机器学习系统的社会影响尤为关键。