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[2602.19028] The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Summary
This paper explores machine learning through a Heideggerian lens, highlighting insights on algorithmic opacity, the limitations of calculation, and the absence of existential structure in AI systems.
Why It Matters
Understanding the philosophical implications of machine learning practices can enhance practitioners' reflexivity and promote a more holistic approach to data science education. This perspective encourages critical engagement with the tools and methodologies used in AI, fostering a deeper awareness of their societal impact.
Key Takeaways
- Machine learning algorithms operate with an automated and opaque metaphysics.
- Technical advancements in AI often reinforce calculative frameworks without questioning their validity.
- AI systems lack existential structure, which limits their ability to reflect on their optimization processes.
- Data science education should include ontological literacy alongside technical skills.
- Philosophical perspectives can enrich the understanding of AI's societal implications.
Computer Science > Computers and Society arXiv:2602.19028 (cs) [Submitted on 25 Nov 2025] Title:The Metaphysics We Train: A Heideggerian Reading of Machine Learning Authors:Heman Shakeri View a PDF of the paper titled The Metaphysics We Train: A Heideggerian Reading of Machine Learning, by Heman Shakeri View PDF HTML (experimental) 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,...
