AI As Epistemic Enframing:
TL;DR
This study argues that AI embodies a calculative mode of thought that reduces knowledge to data, patterns, and predictive models, treating both the world and human subjects as resources for optimization, and proposes Heideggerian meditative thinking (Gelassenheit) as a framework for preserving human insight, reflection, and responsibility in the deployment of intelligent systems.
Abstract
This paper examines artificial intelligence (AI) as a distinctive epistemic regime through the lens of Martin Heidegger's philosophy of technology. While contemporary AI discourse emphasizes performance, ethics, and applications, it often neglects the underlying assumptions about knowledge and truth. Drawing on Heidegger's concepts of enframing (Gestell) and standing-reserve (Bestand), this study argues that AI embodies a calculative mode of thought that reduces knowledge to data, patterns, and predictive models, treating both the world and human subjects as resources for optimization. Methodologically, the paper employs close textual analysis of Heidegger alongside interdisciplinary AI literature to elucidate how machine learning and algorithmic systems enact epistemic enframing. It demonstrates that AI not only reorganizes factual knowledge but reshapes human cognition, language, and social authority, producing a regime of "algorithmic truth" where verification is statistical rather than interpretive. By highlighting the epistemological dimensions of AI, this study extends prior work on AI ethics and societal impact, revealing the ontological stakes of computational knowledge. It further proposes Heideggerian meditative thinking (Gelassenheit) as a framework for preserving human insight, reflection, and responsibility in the deployment of intelligent systems. The paper contributes a novel perspective that situates AI's influence not merely on action but on the very structures of knowing.