Critiquing Human Judgment Using Knowledge-Acquisition Systems
Critiquing Human Judgment Using Knowledge-Acquisition Systems
复制标题
使用知识获取系统批判人类判断
DOI：
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发表时间：
1990
期刊：
影响因子：
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通讯作者：
B. Silverman
中科院分区：
文献类型：
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作者：
B. Silverman
自动化知识获取系统专注于在其软件中嵌入关键知识工作者的认知模型，使系统能够像知识工作者一样通过采访领域专家来获取知识库。出现了两组研究问题：（1）什么理论、策略和方法可以促进建模过程；加速；并且可能是自动化的？如果自动化知识获取系统减少了获取知识库的瓶颈，那么如何打破构建自动化知识获取系统本身的瓶颈呢？ (2) 如果自动化知识获取系统以关键知识工作者的有效认知模型为中心，那么该模型在多大程度上解释并试图影响知识库规则生成中的人类偏见？也就是说，人类在判断过程中容易出现错误和认知偏差。自动化系统如何以积极的方式批评和影响此类偏见，应用程序中存在哪些常见模式，以及影响行为的模型是否可以描述和标准化？本文通过呈现几个描述偏见和去偏见策略的原型场景来回答这些研究问题。
Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? (2) If the automated knowledge-acquisition system centers on having an effective cognitive model of the key knowledge worker(s), to what extent does this model account for and attempt to influence human bias in knowledge base rule generation? That is, humans are known to be subject to errors and cognitive biases in their judgment processes. How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized? This article answers these research questions by presenting several prototypical scenes depicting bias and debiasing strategies.