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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通过减少噪音来改善工作场所的判断：一个世纪的选择研究中的经验教训


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最重要的错误来源。我们认为这个问题是在近 100 年前在人员选拔领域发现的，并且一个世纪的选拔研究表明，通过构建过程（即分解组成部分、商定标准并始终如一地应用这些标准）和独立汇总判断，可以明显减少噪音。算法可以在此过程中提供很大帮助，但经常与当前形式的方法（例如人工智能和机器学习）混淆，这些方法会大大增加判断噪音。