用机器偏见反推人类决策偏见，解决无金标准下的公平性评估难题。
Using Machine Bias To Measure Human Bias
- 基于机器学习构建无金标准的偏见评估框架
- 理论与实证证明优于现有方法，可识别群体间误判差异
- 适用于管理决策透明化及算法训练数据质量改进
人类决策中的偏见在多个领域产生深远影响，导致个体不公平对待，并使组织和社会陷入次优结果。尽管组织常设计干预措施以减轻偏见，但衡量人类决策偏见仍是一项重要却难以实现的任务。由于缺乏金标准标签（即正确决策的参考），通常无法判断某一群体是否被错误地更频繁地误判。本文提出一种基于机器学习的框架，在金标准缺失的情况下评估人类决策中的偏见。我们提供了理论保证和实证证据，证明该方法优于现有替代方案。该方法为人类决策透明度奠定了基础，对管理职责具有重要意义，并有助于在用人类决策作为算法训练标签时缓解算法偏见。
原文摘要 · Abstract (English)
Biased human decisions have consequential impacts across various domains, yielding unfair treatment of individuals and resulting in suboptimal outcomes for organizations and society. In recognition of this fact, organizations regularly design and deploy interventions aimed at mitigating these biases. However, measuring human decision biases remains an important but elusive task. Organizations are frequently concerned with mistaken decisions disproportionately affecting one group. In practice, however, this is typically not possible to assess due to the scarcity of a gold standard: a label that indicates what the correct decision would have been. In this work, we propose a machine learning-based framework to assess bias in human-generated decisions when gold standard labels are scarce. We provide theoretical guarantees and empirical evidence demonstrating the superiority of our method over existing alternatives. This proposed methodology establishes a foundation for transparency in human decision-making, carrying substantial implications for managerial duties, and offering potential for alleviating algorithmic biases when human decisions are used as labels to train algorithms.
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