将哲学思想融入机器学习，让模型自带伦理设计。
Philosophy-informed Machine Learning
- 把分析哲学核心概念嵌入模型架构与评估标准
- 提出可后置应用或内建的两种实践路径
- 推动安全、有伦理意识的AI发展，适合研究者与开发者
哲学引导的机器学习（PhIML）直接将分析哲学的核心思想融入机器学习的模型架构、目标函数和评估协议中，使模型从设计上就尊重哲学概念与价值。本文从这一视角出发，梳理理论基础，展示哲学层面的增益与对齐效果。此外，通过案例研究说明ML从业者可将PhIML作为通用后置工具使用，或将其内在集成到模型架构中。最后，文章揭示了当前面临的技术瓶颈，以及哲学、实践与治理层面的挑战，并勾勒出迈向安全、哲学敏感且伦理负责的PhIML的研究路线图。
原文摘要 · Abstract (English)
Philosophy-informed machine learning (PhIML) directly infuses core ideas from analytic philosophy into ML model architectures, objectives, and evaluation protocols. Therefore, PhIML promises new capabilities through models that respect philosophical concepts and values by design. From this lens, this paper reviews conceptual foundations to demonstrate philosophical gains and alignment. In addition, we present case studies on how ML users/designers can adopt PhIML as an agnostic post-hoc tool or intrinsically build it into ML model architectures. Finally, this paper sheds light on open technical barriers alongside philosophical, practical, and governance challenges and outlines a research roadmap toward safe, philosophy-aware, and ethically responsible PhIML.
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