The Metaphysics We Train: A Heideggerian Reading of Machine Learning

2025-11-25

AI Summary

This study uncovers the implicit metaphysical presuppositions embedded in machine learning practice and their constraints on technological development. Drawing on Heideggerian phenomenology, it offers the first systematic analysis of how algorithmic “projection” (Entwurf) remains confined within the framework of “enframing” (Gestell), lacking essential ontological structures such as “care” (Sorge) and “anxiety” (Angst) that would enable critical reflection on its own optimization logic. By integrating phenomenology with critical theory of technology, the research elucidates the philosophical roots of AI’s inherent limitations, urging practitioners to reflect on the worldviews embedded in their technical systems. Furthermore, it proposes a novel pedagogical pathway for AI ethics and data science education that incorporates ontological literacy, fostering deeper engagement with the existential dimensions of artificial intelligence.

Technology Category

• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Machine Learning: Other Foundations of Machine Learning
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• metaphysics
• machine learning
• Heidegger
• Enframing
• Care

Innovation

• Heideggerian phenomenology
• ontological literacy
• algorithmic Entwurf
• Gestell
• Care (Sorge)