Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics may not fully capture the real-world consequences of unfairness. In this article, we adopt a utility-based approach to more accurately measure the real-world impacts of decision-making process. In particular, we show that if the concept of $\varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. Additionally, we address the common issue of unavailable data on false negatives by proposing a reduced setting that still captures essential fairness considerations. We illustrate our findings with two real-world examples: college admissions and credit risk assessment. Our analysis reveals that while traditional probability-based evaluations might suggest fairness, a utility-based approach uncovers the necessary actions to truly achieve equality. For instance, in the college admission case, we find that enhancing completion rates is crucial for ensuring fairness. Summarizing, this paper highlights the importance of considering the real-world context when evaluating fairness.

翻译：决策过程中的公平性通常使用概率度量进行量化。然而，这些度量可能无法完全捕捉不公平性在现实世界中的实际后果。本文采用基于效用的方法，以更准确地衡量决策过程在现实世界中的影响。具体而言，我们证明，若采用ε-公平性这一概念，可能导致在现实情境下产生最大程度不公平的结果。此外，针对假阴性数据通常不可得这一常见问题，我们提出了一种简化设定，该设定仍能捕捉公平性考量的核心要素。我们通过两个现实案例阐明研究发现：大学招生与信用风险评估。分析表明，虽然传统的基于概率的评估可能暗示公平性，但基于效用的方法揭示了真正实现平等所必需的行动。例如，在大学招生案例中，我们发现提高毕业率对于确保公平至关重要。总而言之，本文强调了在评估公平性时考虑现实情境的重要性。