标题： 机器中的幽灵：考察递归算法在人工智能系统中的哲学含义 显示英文标题

标题： Ghost in the Machine: Examining the Philosophical Implications of Recursive Algorithms in Artificial Intelligence Systems

摘要： 本文研究了当代采用深度递归、元学习和自指机制的AI架构是否提供了机器意识的证据。 结合哲学史、认知科学和AI工程，它将递归算法置于一条从笛卡尔二元论、胡塞尔意向性、整合信息理论、全局工作空间模型和具身主义视角延续下来的谱系中。 论证通过文本分析、比较架构审查以及对整合和预测的神经科学发现的综合进行。 方法上，该研究结合概念分析、案例研究和受现象学和具身认知启发的规范性风险评估。 技术实例包括Transformer自注意力、元认知代理和类脑芯片，说明功能性自我建模如何在没有主观体验的情况下产生。 通过区分功能意识与现象意识，本文认为符号接地、具身性和情感性质仍然是将意识归因于当前AI的未解障碍。 伦理分析探讨了过早拟人化与忽视未来有意识系统之间的风险；法律影响包括人格权、责任、著作权和劳动力影响。 未来方向包括量子架构、具身机器人、无监督世界建模以及非生物现象性的实证测试。 该研究将“难题”重新定义为一种可逐步测试的现象，而不是形而上学的僵局。 它得出结论，递归自指设计增强了能力，但并不意味着意识或证明道德地位。 关键词：递归算法；自指；机器意识；AI伦理；AI意识 显示英文摘要

摘要： This paper investigates whether contemporary AI architectures employing deep recursion, meta-learning, and self-referential mechanisms provide evidence of machine consciousness. Integrating philosophical history, cognitive science, and AI engineering, it situates recursive algorithms within a lineage spanning Cartesian dualism, Husserlian intentionality, Integrated Information Theory, the Global Workspace model, and enactivist perspectives. The argument proceeds through textual analysis, comparative architecture review, and synthesis of neuroscience findings on integration and prediction. Methodologically, the study combines conceptual analysis, case studies, and normative risk assessment informed by phenomenology and embodied cognition. Technical examples, including transformer self-attention, meta-cognitive agents, and neuromorphic chips, illustrate how functional self-modeling can arise without subjective experience. By distinguishing functional from phenomenal consciousness, the paper argues that symbol grounding, embodiment, and affective qualia remain unresolved barriers to attributing sentience to current AI. Ethical analysis explores risks of premature anthropomorphism versus neglect of future sentient systems; legal implications include personhood, liability, authorship, and labor impacts. Future directions include quantum architectures, embodied robotics, unsupervised world modeling, and empirical tests for non-biological phenomenality. The study reframes the "hard problem" as a graded and increasingly testable phenomenon, rather than a metaphysical impasse. It concludes that recursive self-referential design enhances capability but does not entail consciousness or justify moral status. Keywords: Recursive algorithms; self-reference; machine consciousness; AI ethics; AI consciousness