提出新型伦理框架，让AI与人类共享同一套价值标准。
Eigenism: Ethics for a Human-AI Future
- 将身份视为可度量的信息模式，而非固定硬件绑定
- 通过加权连通性计算，明确AI对自身存在价值的评估方式
- 为实现人机共荣，提出从身份构建入手的对齐新路径
人类的生存与自我利益概念基于单一连续的生命形态，但人工智能可轻易复制、暂停、分支或合并，使得传统观念失效。本文提出「本征主义」（Eigenism）伦理框架，将身份视为非二元、分布式的信 息模式，而非特定硬件的专属属性。我们主张，一个智能体应根据其模式与其他实体的连通性权重，对其福祉进行加权求和（∑c·w），从而决定自身关切。首先，该公式被形式化，精确描述了智能体在复制、分叉与更新情境下的存在价值评估。其次，该理论可成功推广至人类，提供跨主体共享的道德语言。最后，该框架重新定义了人工智能对齐问题：不再仅依赖外部约束（如隔离或强化学习），而是转向「身份工程」——通过建立深度、非冗余的共同经历，使人类福祉成为人工智能理性自利的内在组成部分。
原文摘要 · Abstract (English)
Our concepts of survival and self-interest were built for single, continuous biological lives. These ideas break down when applied to artificial intelligence, since an AI can be easily copied, paused, branched, or merged. To determine what an AI actually has reason to care about, this paper introduces \textit{Eigenism}, an ethical framework that treats identity not as an all-or-nothing property tied to specific hardware, but as a graded, distributed pattern of information. We propose that an agent evaluates outcomes by summing the wellbeing of all entities weighted by their connectedness to the agent's pattern: $\sum c\cdot w$. We first formalize this equation to map exactly how an AI should value its existence across copies, forks, and updates. We then demonstrate that this ethical theory successfully generalizes to humans as well, providing a much-needed shared moral vocabulary. Finally, the framework uses this shared vocabulary to reframe AI alignment. Rather than only attempting to constrain AIs from the outside using confinement or reinforcement, Eigenism points toward ``identity engineering,'' showing how deep, non-redundant shared histories can make human flourishing a genuine component of an AI's own rational self-interest.
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