In the interdisciplinary field of artificial intelligence (AI) the problem of clear terminology is especially momentous. This paper claims, that AI debates are still characterised by a lack of critical distance to metaphors like 'training', 'learning' or 'deciding'. As consequence, reflections regarding responsibility or potential use-cases are greatly distorted. Yet, if relevant decision-makers are convinced that AI can develop an 'understanding' or properly 'interpret' issues, its regular use for sensitive tasks like deciding about social benefits or judging court cases looms. The chapter argues its claim by analysing central notions of the AI debate and tries to contribute by proposing more fitting terminology and hereby enabling more fruitful debates. It is a conceptual work at the intersection of critical computer science and philosophy of language.

翻译：在人工智能这一跨学科领域中，清晰术语的问题尤为关键。本文指出，人工智能辩论仍然缺乏对"训练"、"学习"或"决策"等隐喻的批判性距离。其后果是，关于责任或潜在应用场景的反思被严重扭曲。然而，如果相关决策者相信人工智能能够发展出"理解"或正确"解释"问题，那么它在诸如判定社会福利或审理法律案件等敏感任务中的常规使用便迫在眉睫。本文通过分析人工智能辩论中的核心概念来论证其观点，并试图通过提出更恰当的术语来促成更富有成效的辩论。这是一项处于批判性计算机科学与语言哲学交叉领域的概念性工作。