AI越聪明，人越难理解；论文提出必须守住人类认知底线。
Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
- 定义认知完整性阈值（CIT），明确人类需保留的最低理解力
- 指出当前透明度设计无法保障长期监督的有效性
- 提出三维度框架，支持责任关键场景下的可持续人机协作
AI系统日益生成流畅且正确的端到端结果，但长期使用会削弱用户解释、验证或干预的能力。本文将这种能力与理解力的脱节称为‘能力-理解差距’：在辅助性能提升的同时，用户的内部模型持续恶化。现有透明度、控制、素养和治理方法未能界定人类在持续依赖AI时所需的基础理解力。为此，本文提出认知完整性阈值（CIT），即在AI协助下维持监督权、自主性和可问责参与所需的最低理解水平。CIT不需完全重建推理过程，也不限制自动化，而是识别出监督沦为形式化程序、争议无法成立的临界点。通过三个功能维度实现CIT：(i) 验证能力，(ii) 保持理解的交互机制，(iii) 治理的制度性支撑。这推动了一种契合责任关键场景中认知可持续性的设计与治理议程。
原文摘要 · Abstract (English)
AI systems increasingly produce fluent, correct, end-to-end outcomes. Over time, this erodes users' ability to explain, verify, or intervene. We define this divergence as the Capability-Comprehension Gap: a decoupling where assisted performance improves while users' internal models deteriorate. This paper argues that prevailing approaches to transparency, user control, literacy, and governance do not define the foundational understanding humans must retain for oversight under sustained AI delegation. To formalize this, we define the Cognitive Integrity Threshold (CIT) as the minimum comprehension required to preserve oversight, autonomy, and accountable participation under AI assistance. CIT does not require full reasoning reconstruction, nor does it constrain automation. It identifies the threshold beyond which oversight becomes procedural and contestability fails. We operatinalize CIT through three functional dimensions: (i) verification capacity, (ii) comprehension-preserving interaction, and (iii) institutional scaffolds for governance. This motivates a design and governance agenda that aligns human-AI interaction with cognitive sustainability in responsibility-critical settings.
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