Increases in the deployment of machine learning algorithms for applications
that deal with sensitive data have brought attention to the issue of fairness
in machine learning. Many works have been devoted to applications that require
different demographic groups to be treated fairly. However, algorithms that aim
to satisfy inter-group fairness (also called group fairness) may inadvertently
treat individuals within the same demographic group unfairly. To address this
issue, we introduce a formal definition of within-group fairness that maintains
fairness among individuals from within the same group. We propose a
pre-processing framework to meet both inter- and within-group fairness criteria
with little compromise in accuracy. The framework maps the feature vectors of
members from different groups to an inter-group-fair canonical domain before
feeding them into a scoring function. The mapping is constructed to preserve
the relative relationship between the scores obtained from the unprocessed
feature vectors of individuals from the same demographic group, guaranteeing
within-group fairness. We apply this framework to the COMPAS risk assessment
and Law School datasets and compare its performance in achieving inter-group
and within-group fairness to two regularization-based methods.

通过引入在组公平性的形式定义，提出了一种预处理框架，以实现组间和组内公平性，并在维持准确性的同时，保证了来自同一族群的个体之间的公平性。将此框架应用于 COMPAS 风险评估和法学院数据集，并将其在实现组间和组内公平性方面的性能与两种基于正则化的方法进行了比较。

实现组内与组间公平性的规范数据转换

A Canonical Data Transformation for Achieving Inter- and Within-group  Fairness

Graph neural network (GNN) link prediction is increasingly deployed in
citation, collaboration, and online social networks to recommend academic
literature, collaborators, and friends. While prior research has investigated
the dyadic fairness of GNN link prediction, the within-group fairness and
``rich get richer'' dynamics of link prediction remain underexplored. However,
these aspects have significant consequences for degree and power imbalances in
networks. In this paper, we shed light on how degree bias in networks affects
Graph Convolutional Network (GCN) link prediction. In particular, we
theoretically uncover that GCNs with a symmetric normalized graph filter have a
within-group preferential attachment bias. We validate our theoretical analysis
on real-world citation, collaboration, and online social networks. We further
bridge GCN's preferential attachment bias with unfairness in link prediction
and propose a new within-group fairness metric. This metric quantifies
disparities in link prediction scores between social groups, towards combating
the amplification of degree and power disparities. Finally, we propose a simple
training-time strategy to alleviate within-group unfairness, and we show that
it is effective on citation, online social, and credit networks.

该研究探讨了图神经网络在链接预测中的群内公平性和偏好附着偏差，提出了新的群内公平度量，并提出了简单的训练策略以减轻群内不公平性。