AI 中文总结
本文构建源中立的技术介导心智状态归因监管方案，划分监管对象、损害路径与义务层级，为神经技术伦理监管提供非约束性分类工具。
AI 中文摘要
两个系统可向同一机构决策者提供与同一主体相关的相同归因结果，但却分属不同法律类别：一个使用神经信号，另一个使用文本或行为。因此，源绑定规则可能被规避，而“心智数据”这一通用类别则存在将不可靠输出视为心智事实的风险。本文将2025年《联合国教科文组织神经技术伦理建议》解读为非约束性指南，为技术介导的、与主体相关的心智状态归因构建源中立触发机制。通过选择性批判性综合、概念建构、功能法律比较及匹配反事实案例，本文将引出、归因与使用划分为递进式监管对象，还从两组独立评估的义务序列中区分出两种损害路径。七个有序问题与两个升级谓词将允许的实践分配至三个义务层级：推定禁止适用于实质性影响自主权的非合意闭环干预，以及隐蔽或强制推断、核心属性、后果性决策与操纵的特定组合。共同进入不会消除与源相关的加重情节：侵入性、具身性与闭环能力可增加义务或设定更高最低要求，而后果性非神经推断可达到相同层级。该方案是一种分类工具，而非经实证验证的监管结果。
英文摘要
Two systems can supply the same person-linked attribution to the same institutional decision maker yet fall into different legal categories: one uses neural signals, the other text or behaviour. A source-bound rule therefore permits circumvention, while an all-purpose category of "mental data" risks treating fallible outputs as facts about the mind. This article reads the 2025 UNESCO Recommendation on the Ethics of Neurotechnology as non-binding guidance and develops a source-neutral trigger for technologically mediated, person-linked mental-state attribution. Through selective critical synthesis, conceptual engineering, functional legal comparison, and matched counterfactual cases, it separates elicitation, attribution, and use as cumulative objects of regulation. The analysis also distinguishes two harm pathways from two independently assessed duty series. Seven ordered questions and two escalation predicates assign permitted practices to three duty tiers. Presumptive prohibition is reserved for materially autonomy-affecting non-consensual closed-loop intervention and for specified combinations of covert or coercive inference, core attributes, consequential decisions, and manipulation. Common entry does not erase source-related aggravators: invasiveness, embodiment, and closed-loop capacity can add duties or set a higher minimum, while consequential non-neural inference can reach the same tier. The resulting scheme is a classification device, not an empirically validated regulatory outcome.
Comments43 pages, 4 tables
