提出人机协作决策框架，解决企业知识如何存、谁来决策的问题。
Do we have the knowledge we need? Rethinking human-AI decision-making in corporations
- 构建任务属性与知识可得性映射的决策代理分配框架。
- 在质检和厂址选择中验证框架，适配不同风险与不确定性场景。
- 适合研究组织智能化转型或人机协同机制的学者与管理者。
组织知识分散在各类软件系统、隐性经验与人工文档中，传统上面向人类使用设计。随着AI系统被越来越多地赋予决策角色，需获取这些知识。这引发两个问题：组织应如何存储和维护知识，以确保人类与未来AI系统均可访问？在不同风险与不确定性水平的任务中，人类与AI的决策权应如何分配？本文描述组织知识的演化过程，提出一个将任务属性与知识可用性映射到推荐决策代理分配与控制机制的框架。通过在两种制造任务中的应用——例行操作（视觉质量检验）与一次性战略决策（工厂选址），展示该框架的适用性，并展望未来研究方向。
原文摘要 · Abstract (English)
Organizational knowledge is fragmented across a variety of software systems, tacit expertise, and manual documents that have traditionally been designed for human consumption. As AI systems are increasingly deployed and granted decision-making roles, they require access to this knowledge. This raises two questions: how should organizations store and maintain knowledge so that it remains accessible to both humans and future AI systems, and how should agency be allocated between humans and AI across tasks with different risks and levels of uncertainty? In this position paper, we describe how organizational knowledge evolves and contribute a framework that maps task attributes and knowledge availability to recommended agency allocations and control mechanisms. We illustrate the applicability of the framework on two different manufacturing tasks: a routine operation (visual quality inspection) and a one-off strategic decision (factory location), and conclude with opportunities for future research.
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