Abstract
This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded and philosophically coherent analysis of regulatory blind spots. Our central claim is that what remains absent from policymaking is an account of the dynamic of becoming that underpins both the technical operation and economic logic of AI. To address this, we advance a formal reading of AI inspired by Simondonian philosophy of technology, reworking his concept of individuation to model the AI lifecycle, including the pre-individual milieu, individuation, and individuated AI. To translate these ideas, we introduce futurity: the self-reinforcing lifecycle of AI, where more data enhances performance, deepens personalisation, and expands application domains. Futurity highlights the recursively generative, non-rivalrous nature of data, underpinned by infrastructures like feature stores that enable feedback, adaptation, and temporal recursion. Our intervention foregrounds escalating power asymmetries, particularly the tech oligarchy whose infrastructures of capture, training, and deployment concentrate value and decision-making. We argue that effective regulation must address these infrastructural and temporal dynamics, and propose measures including lifecycle audits, temporal traceability, feedback accountability, recursion transparency, and a right to contest recursive reuse.
Abstract (translated)
这篇论文主张，从技术哲学的角度解读欧盟《人工智能法案》可以为理解AI系统中数据的长期动态提供洞见，特别是数据从摄取到部署的生命循环如何生成递归价值链，从而挑战现有的负责任AI框架。我们引入了一种概念工具来构建涵盖数据、训练制度、架构、特征存储和迁移学习的人工智能管道模型。通过跨学科的方法，我们在技术实证的基础上进行哲学上的分析，揭示监管的盲点所在。我们的核心观点是，在政策制定中缺乏对支撑人工智能技术和经济逻辑的发展过程的理解。 为了解决这个问题，我们借鉴吉伦·菲利蒙（Simondonian）的技术哲学思想提出了一个形式化的解读视角，并重新定义了他的个体化概念以建模AI生命周期，包括前个人环境、个体化以及个体化的人工智能。为了表达这些观念，我们将引入“未来性”：即人工智能自我强化的生命循环，在这种循环中，更多的数据可以提高性能，加深个性化并扩展应用场景。“未来性”突出了基于特征存储等基础设施支持的反馈机制和时间递归下所体现的数据的递归生成、非竞争性质。 我们的研究强调了不断加剧的权力不对称问题，尤其是技术寡头通过捕获、训练及部署的基础设施集中价值与决策权。我们主张有效的监管必须解决这些基础设施和时间动态的问题，并提出包括生命周期审计、时序可追溯性、反馈问责制、递归透明度以及针对循环再用的质疑权利在内的措施。
URL
https://arxiv.org/abs/2508.15680
