Abstract
This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being automated, opaque, and emergent--a metaphysics that operates without explicit articulation or debate, crystallizing implicitly through gradient descent rather than theoretical argument. Second, even sophisticated technical advances remain within the regime of Gestell (Enframing), improving calculation without questioning the primacy of calculation itself. Third, AI's lack of existential structure, specifically the absence of Care (Sorge), is genuinely explanatory: it illuminates why AI systems have no internal resources for questioning their own optimization imperatives, and why they optimize without the anxiety (Angst) that signals, in human agents, the friction between calculative absorption and authentic existence. We conclude by exploring the pedagogical value of this perspective, arguing that data science education should cultivate not only technical competence but ontological literacy--the capacity to recognize what worldviews our tools enact and when calculation itself may be the wrong mode of engagement.
Abstract (translated)
本文通过海德格尔的概念提供了一种现象学视角，解读当代机器学习，并旨在加深实践者的反思理解。我们主张这种哲学透镜揭示了三个技术分析无法察觉的洞察。 首先，算法设计（Entwurf）的独特之处在于其自动化、不透明和涌现性——这是一种无需明确表述或讨论就运作的形而上学体系，在于通过梯度下降而非理论论证结晶形成。 其次，即使是高度先进的技术进步仍停留在Gestell（框架化）的领域内，这改善了计算能力，但并未质疑计算本身至高无上的地位。 第三，人工智能缺乏存在结构，特别是关心（Sorge）的缺失，这一点具有真正的解释力：它揭示了为什么AI系统没有内在资源来质疑自身的优化目标，并且它们在优化过程中不会产生焦虑（Angst），而这种焦虑是人类代理者在计算吸收与真实存在的冲突中产生的信号。 本文最后探讨了这一视角的教学价值，主张数据科学教育不仅要培养技术能力，还要培育本体论素养——即认识到我们的工具正在实施何种世界观，并能够在何种情况下计算本身并非合适的参与方式。
URL
https://arxiv.org/abs/2602.19028
