位置：人本AI需要最低可行的人类理解水平
Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
AI总结：
本文提出认知完整性阈值（CIT）作为人本AI中保持监督和问责的关键标准，强调人类必须保留的最低理解水平以应对持续的人工智能委托。
摘要：
人工智能系统越来越多地产生流畅、正确、端到端的结果。随着时间的推移，这种趋势削弱了用户解释、验证或干预的能力。我们定义这种分歧为能力-理解差距：一种解耦，其中辅助性能提高，而用户的内部模型恶化。本文认为，现有的透明度、用户控制、素养和治理方法并未定义人类在持续的人工智能委托下必须保留的基础理解。为了正式化这一点，我们定义了认知完整性阈值（CIT）作为在人工智能协助下保持监督、自主和可问责参与所需的最低理解水平。CIT不需要完整的推理重建，也不限制自动化。它确定了监督变得程序化和可争议性失效的阈值。我们通过三个功能维度来操作化CIT：（i）验证能力，（ii）保持理解的互动，以及（iii）治理的制度支架。这促使了一种设计和治理议程，使人类-人工智能互动与责任关键环境中的认知可持续性对齐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
