In recent years, machine learning algorithms have become ubiquitous in a
multitude of high-stakes decision-making applications. The unparalleled ability
of machine learning algorithms to learn patterns from data also enables them to
incorporate biases embedded within. A biased model can then make decisions that
disproportionately harm certain groups in society -- limiting their access to
financial services, for example. The awareness of this problem has given rise
to the field of Fair ML, which focuses on studying, measuring, and mitigating
unfairness in algorithmic prediction, with respect to a set of protected groups
(e.g., race or gender). However, the underlying causes for algorithmic
unfairness still remain elusive, with researchers divided between blaming
either the ML algorithms or the data they are trained on. In this work, we
maintain that algorithmic unfairness stems from interactions between models and
biases in the data, rather than from isolated contributions of either of them.
To this end, we propose a taxonomy to characterize data bias and we study a set
of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML
algorithms exhibit under different data bias settings. On our real-world
account-opening fraud use case, we find that each setting entails specific
trade-offs, affecting fairness in expected value and variance -- the latter
often going unnoticed. Moreover, we show how algorithms compare differently in
terms of accuracy and fairness, depending on the biases affecting the data.
Finally, we note that under specific data bias conditions, simple
pre-processing interventions can successfully balance group-wise error rates,
while the same techniques fail in more complex settings.

机器学习算法应用广泛，但也可能存在偏见和不公平。本研究提出了一种刻画数据偏见的分类法，并探究了算法的不公平和精度之间的权衡。实证研究表明，在不同的数据偏见设置下，算法的公平性和精度会受到不同的影响，而简单的预处理干预技术可以提高算法的公平性。

通过模型和数据偏差交互理解欺诈检测中的不公平性

Understanding Unfairness in Fraud Detection through Model and Data Bias  Interactions

In response to algorithmic unfairness embedded in sociotechnical systems,
significant attention has been focused on the contents of machine learning
datasets which have revealed biases towards white, cisgender, male, and Western
data subjects. In contrast, comparatively less attention has been paid to the
histories, values, and norms embedded in such datasets. In this work, we
outline a research program - a genealogy of machine learning data - for
investigating how and why these datasets have been created, what and whose
values influence the choices of data to collect, the contextual and contingent
conditions of their creation. We describe the ways in which benchmark datasets
in machine learning operate as infrastructure and pose four research questions
for these datasets. This interrogation forces us to "bring the people back in"
by aiding us in understanding the labor embedded in dataset construction, and
thereby presenting new avenues of contestation for other researchers
encountering the data.

研究了机器学习数据集背后的历史、价值观和规范，以及影响数据收集选择的价值观，并描述了机器学习中基准数据集的运作方式和为这些数据集提出的四个研究问题，从而更好地了解数据构建中的劳动，为研究人员提供新的争论途径。

让人民参与进来：争夺基准机器学习数据集

Bringing the People Back In: Contesting Benchmark Machine Learning  Datasets

Discrimination via algorithmic decision making has received considerable
attention. Prior work largely focuses on defining conditions for fairness, but
does not define satisfactory measures of algorithmic unfairness. In this paper,
we focus on the following question: Given two unfair algorithms, how should we
determine which of the two is more unfair? Our core idea is to use existing
inequality indices from economics to measure how unequally the outcomes of an
algorithm benefit different individuals or groups in a population. Our work
offers a justified and general framework to compare and contrast the
(un)fairness of algorithmic predictors. This unifying approach enables us to
quantify unfairness both at the individual and the group level. Further, our
work reveals overlooked tradeoffs between different fairness notions: using our
proposed measures, the overall individual-level unfairness of an algorithm can
be decomposed into a between-group and a within-group component. Earlier
methods are typically designed to tackle only between-group unfairness, which
may be justified for legal or other reasons. However, we demonstrate that
minimizing exclusively the between-group component may, in fact, increase the
within-group, and hence the overall unfairness. We characterize and illustrate
the tradeoffs between our measures of (un)fairness and the prediction accuracy.

本文提出了一种将经济学中的不平等指数应用于算法不公平度量的框架，该框架可以同时量化个体和群体层面上的不公平，同时揭示了不同公平概念之间的权衡。