让AI的隐性知识可被人类验证，提升可靠性
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
- 提出知识对象（KOs）结构化外化隐性知识
- 使人类能验证推理与判断过程，降低错误风险
- 适合关注AI可信性与人机协作的研究者
本文主张，可靠AI需要建立对隐性知识的人类验证机制。当前AI既学习显性知识（论文、文档、数据库），也吸收隐性知识（推理模式、调试过程、中间步骤），但后者因记录成本高于感知价值而未被外部化。然而AI仍无差别地学习这些内容，既获得有益模式，也继承有害偏见。现有可靠性方法仅能验证显性知识来源，无法检验最关键的智能能力——推理、判断与直觉，形成根本性缺口。为此我们提出知识对象（KOs），将隐性知识转化为人类可检查、验证与认可的结构化载体。KOs重构验证经济：原本过高的验证成本变得可行，通过持续积累的人类验证，实现可靠性长期提升。
原文摘要 · Abstract (English)
This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, structured databases) and implicit knowledge (reasoning patterns, debugging processes, intermediate steps). Implicit knowledge remains unexternalized because documentation cost exceeds perceived value -- yet AI learns from it indiscriminately, acquiring both beneficial patterns and harmful biases. Current reliability methods can only verify explicit knowledge against sources, creating a fundamental gap: the most valuable AI capabilities (reasoning, judgment, intuition) are precisely those we cannot verify. We propose Knowledge Objects (KOs) -- structured artifacts that externalize implicit knowledge into forms humans can inspect, verify, and endorse. KOs transform verification economics: what was previously too costly to verify becomes feasible, enabling accumulated human validation to improve reliability over time.
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