Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of (new) social inequalities through machine learning-based systems.

机器学习已成为当代信息系统的关键组成部分。不同于以正式语言显式编程的现有信息系统，机器学习系统从数据推断规则。本文说明了这种差异对基于机器学习的社会技术系统的批判性分析意味着什么。为了为将来基于机器学习的系统的关键分析提供基础，我们参与了如何在自我教育资源中构建和构建该术语的方法。为此，我们分析了机器学习教程，这是自学者的重要信息来源，也是形成机器学习社区实践的关键工具。我们的分析确定了机器学习的典型示例以及重要的误解和有问题的框架。我们的结果表明，机器学习被认为具有普遍适用性，并且积极鼓励没有特殊专业知识的机器学习的应用。机器学习算法的解释缺失或受到严格限制。同时，数据的重要性被大大低估了。通过基于机器学习的系统，这对（新的）社会不平等现象的表现具有影响。