Some assert that noise (i.e., unwanted variance) is the most neglected yet most important source of error in judgment. We suggest that this problem was discovered nearly 100 years ago in the area of personnel selection and that a century of selection research has shown that noise can be demonstrably reduced by structuring the process (i.e., decomposing the component parts, agreeing on standards, and applying those standards consistently) and by aggregating judgments independently. Algorithms can aid significantly in this process but are often confused with methods that, in their current form, can substantially increase noise in judgment (e.g., artificial intelligence and machine learning).

中文翻译：

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通过减少噪音提高工作场所判断力：一个世纪选择研究的经验教训

一些人断言，噪声（即不需要的方差）是判断中最容易被忽视但也是最重要的错误来源。我们认为，这个问题在近 100 年前就在人员选拔领域被发现，一个世纪的选拔研究表明，通过结构化流程（即分解组成部分、商定标准并应用这些标准一致）并通过独立汇总判断。算法可以在这一过程中提供显着帮助，但经常与目前形式的方法（例如人工智能和机器学习）相混淆，这些方法会大大增加判断中的噪音。