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arXiv · 2505.19353

Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation

Camilo Chacón Sartori
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Abstract

With the rise of generative AI (GenAI), Large Language Models are increasingly employed for code generation, becoming active co-authors alongside human programmers. Focusing specifically on this application domain, this paper articulates distinct ``Architectures of Error'' to ground an epistemic distinction between human and machine code generation. Examined through their shared vulnerability to error, this distinction reveals fundamentally different causal origins: human-cognitive versus artificial-stochastic. To develop this framework and substantiate the distinction, the analysis draws critically upon Dennett's mechanistic functionalism and Rescher's methodological pragmatism. I argue that a systematic differentiation of these error profiles raises critical philosophical questions concerning semantic coherence, security robustness, epistemic limits, and control mechanisms in human-AI collaborative software development. The paper also utilizes Floridi's levels of abstraction to provide a nuanced understanding of how these error dimensions interact and may evolve with technological advancements. This analysis aims to offer philosophers a structured framework for understanding GenAI's unique epistemological challenges, shaped by these architectural foundations, while also providing software engineers a basis for more critically informed engagement.

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BibTeXRIS

Camilo Chacón Sartori. 2025-05-25. Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation. https://doi.org/10.1007/s13347-026-01056-x

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