AI 中文总结
该研究指出生成式AI将主流认知编码为知识基础设施的认知从属问题，其危害跨越三类法律领域，现有法律仅监管下游应用，需转向模型训练层面进行规制。
AI 中文摘要
生成式人工智能并非仅产生有偏差的输出，而是将多数人的认知方式编码为知识本身的默认基础设施，我们将这种现象称为认知从属。训练过程将人类表达的全部广度压缩为单个概率模型，其统计基线反映了主流文化的语言、假设和文化框架。少数群体的认知方式并未被排除，而是被吸收：它们存在于训练数据中，但在输出层面受到结构性从属。其结果并非可被审计和纠正的离散偏差，而是嵌入所有输出生成架构的认知状态。这种统一的危害跨越三个法律领域——反歧视法、文化与语言权利、民主观点多元主义——而每一个领域都因相同的结构性原因未能解决该问题：现有法律在下游层面（决策和应用层面）进行监管。补救措施必须与危害发生的位点相匹配：若认知从属产生于模型训练层面，那么法律必须学会在该层面进行监管。
英文摘要
Generative AI does not merely produce biased outputs. It encodes the majority's way of knowing as the default infrastructure of knowledge itself. We call this epistemic subordination. The training process compresses the full breadth of human expression into a single probabilistic model whose statistical baseline reflects the languages, assumptions, and cultural frameworks of the dominant culture. Minority epistemologies are not excluded but absorbed: present in the training data, yet structurally subordinated in the output. The result is not a collection of discrete biases that can be audited and corrected. It is an epistemic condition embedded in the architecture from which all outputs emerge. This unified harm cuts across three legal domains -- anti-discrimination law, cultural and linguistic rights, and democratic viewpoint pluralism -- and each fails to address it for the same structural reason: existing law regulates downstream, at the level of decisions and applications. The remedy must match the site of harm. If epistemic subordination is produced at the level of model training, then law must learn to govern at that level.
