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Algorithms, language, and poetry: a phenomenological perspective

Daniel Turillazzi Fornés, Angelo Trotta

AI and Ethics December 19, 2025 DOI: 10.1007/s43681-025-00948-6 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Keywords Ai ethics Large language models Phenomenology of language Heidegger Merleau-ponty Saussure Enactive ai 4e cognition Media theory
Key points Argues that algorithmic formalizations of language, as seen in LLMs, are historically specific reductions of a more primordial field of embodied expression, and that treating language as optimizable signals risks losing its living, self-renewing character.

Abstract

This paper examines the algorithmic formalization of language through a phenomenological lens, engaging Martin Heidegger and Maurice Merleau-Ponty in dialogue with contemporary large language models (LLMs) and related AI systems. Instead of treating computationally modeled language as a neutral medium for information transfer, we argue that both formal logic and data-driven models are historically specific crystallizations of a more primordial field of embodied expression. The idea of the ”unity of language” refers to the dynamic, historically situated field of expressive possibilities within which multiple linguistic systems — natural languages, formal calculi, code, poetic language — emerge, sediment, and transform. Drawing on Merleau-Ponty’s account of embodied speech, we reconstruct language as a living, self-renewing medium whose unity lies in its ongoing capacity to generate new sense. Heidegger’s analysis of technological ”enframing” (Gestell) and his reflections on ”traditional language” then allow us to interpret algorithmic conceptions of language as powerful, but critically informing of the existential risk of reducing speech to optimizable signals within the wider field of linguistic life. We confront these insights with current developments in AI, including LLMs, embodied AI, and enactive or 4E approaches to cognition. We conclude by sketching phenomenologically informed criteria for language technologies that respect expressive openness, relational depth, and the historicity of signifiers, and indicate how such criteria can orient debates in AI ethics.