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常通过概率指标量化。然而，这些指标可能无法完全反映不公平性在现实世界中的后果。本文采用基于效用的方法，更精确地衡量决策过程对现实世界的影响。具体而言，我们表明，若采用$\varepsilon$-公平概念，可能导致在现实情境中产生最大程度不公平的结果。此外，针对假阴性数据不可用的常见问题，我们提出一种简化设定，仍能保留关键的公平性考量。我们通过两个现实案例——大学录取与信用风险评估——阐明研究发现。分析揭示，传统基于概率的评估可能显示公平性，而基于效用的方法则能揭示实现真正平等所需的必要行动。例如，在大学录取案例中，我们发现提高毕业率对确保公平至关重要。总之，本文强调了评估公平性时考虑现实情境的重要性。