剖析生成式AI与人类编程的错误差异，揭示其认知根源本质不同。
Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation
- 从错误模式出发，区分人类认知与机器随机性的根本差异
- 结合哲学理论，建立理解人机协作编程的元认知框架
- 适合哲学与软件工程交叉研究者参考，启发对AI可靠性的深层思考
随着生成式AI（GenAI）兴起，大语言模型日益被用于代码生成，成为人类程序员的协同作者。本文聚焦该应用场景，提出“错误架构”概念，旨在确立人类与机器代码生成之间的认识论差异。通过分析二者共有的错误脆弱性，揭示其根本成因迥异：人类源于认知过程，机器则来自人工随机性。为构建这一框架并验证差异，研究批判性地借鉴了丹尼特的机械功能主义和雷舍尔的方法论实用主义。论证系统性区分这些错误特征，将引发关于语义连贯性、安全鲁棒性、认识边界与控制机制的关键哲学问题。论文还运用弗洛里迪的抽象层次理论，深入解析错误维度间的交互关系及其随技术演进的潜在变化。本研究旨在为哲学家提供理解生成式AI独特认识论挑战的结构化工具，同时为软件工程师提供更批判性参与人机协同开发的基础。
原文摘要 · Abstract (English)
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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