The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918352668065792 |
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| author | Shakeri, Heman |
| author_facet | Shakeri, Heman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Metaphysics We Train: A Heideggerian Reading of Machine Learning Shakeri, Heman Computers and Society Artificial Intelligence Machine Learning 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. |
| title | The Metaphysics We Train: A Heideggerian Reading of Machine Learning |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.19028 |