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 They can exacerbate existing inequities, create new modes of discrimination, and reify outdated social constructs.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（ML）系统，尤其是在高风险领域部署时，具有深远的影响。它们可能加剧现有不平等、制造新的歧视形式，并使过时的社会建构具体化。因此，开发ML系统的社会背景（即组织、团队、文化）是人工智能伦理领域积极研究的方向，也是政策制定者干预的重点。本文聚焦于社会背景中一个常被忽视的方面：实践者与其依赖工具之间的互动，以及这些互动在塑造ML实践和ML系统开发中所起的作用。具体而言，通过对Stack Exchange论坛问题的实证研究，本文探讨了交互式计算平台（如Jupyter Notebook和Google Colab）在ML实践中的使用。我发现，交互式计算平台被用于一系列学习和协调实践，这构成了交互式计算平台与ML实践者之间的基础设施关系。我描述了ML实践如何与交互式计算平台的发展共同演进，并强调了这种演进可能使ML生命周期中那些被AI伦理研究者证明对部署ML系统的社会影响尤为重要的方面变得隐而不显的风险。