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.

本文研究了决策过程中的公平性，并提出了基于效用的方法来更准确地评估决策过程的真实世界影响。通过两个实际案例研究，发现传统的概率评估可能无法全面捕捉公平性，而基于效用的方法则可以揭示实现平等的必要行动。总结来说，本文强调了在评估公平性时考虑真实世界背景的重要性。

ε- 公平的不公平性

The Unfairness of $\varepsilon$-Fairness

Social media discourse from US politicians frequently consists of 'seemingly
similar language used by opposing sides of the political spectrum'. But often,
it translates to starkly contrasting real-world actions. For instance, "We need
to keep our students safe from mass shootings" may signal either "arming
teachers to stop the shooter" or "banning guns to reduce mass shootings"
depending on who says it and their political stance on the issue. In this
paper, we define and characterize the context that is required to fully
understand such ambiguous statements in a computational setting and ground them
in real-world entities, actions, and attitudes. To that end, we propose two
challenging datasets that require an understanding of the real-world context of
the text to be solved effectively. We benchmark these datasets against
baselines built upon large pre-trained models such as BERT, RoBERTa, GPT-3,
etc. Additionally, we develop and benchmark more structured baselines building
upon existing 'Discourse Contextualization Framework' and 'Political Actor
Representation' models. We perform analysis of the datasets and baseline
predictions to obtain further insights into the pragmatic language
understanding challenges posed by the proposed social grounding tasks.

社交媒体上的言论常常由政治光谱两极中的不同方面使用 “看似相似的语言” 组成，但往往对应着截然不同的现实行动。本文旨在在计算环境中定义和描述理解此类模棱两可陈述所需的背景，并将其与真实世界的实体、行动和态度联系起来。为了达到这个目标，我们提出了两个具有挑战性的数据集，需要对文本的现实背景有全面的理解才能有效解决。我们通过构建基于 BERT、RoBERTa、GPT-3 等大型预训练模型的基线，并基于现有的 “话语语境框架” 和 “政治参与者表示” 模型开发和基准测试了更有结构化的基线。我们对数据集和基线预测进行分析，以深入了解所提出的社交背景任务所带来的实用语言理解挑战。