Machine learning algorithms are increasingly used for consequential decision
making regarding individuals based on their relevant features. Features that
are relevant for accurate decisions may however lead to either explicit or
implicit forms of discrimination against unprivileged groups, such as those of
certain race or gender. This happens due to existing biases in the training
data, which are often replicated or even exacerbated by the learning algorithm.
Identifying and measuring these biases at the data level is a challenging
problem due to the interdependence among the features, and the decision
outcome. In this work, we develop a framework for fairness-aware feature
selection which takes into account the correlation among the features and the
decision outcome, and is based on information theoretic measures for the
accuracy and discriminatory impacts of features. In particular, we first
propose information theoretic measures which quantify the impact of different
subsets of features on the accuracy and discrimination of the decision
outcomes. We then deduce the marginal impact of each feature using Shapley
value function; a solution concept in cooperative game theory used to estimate
marginal contributions of players in a coalitional game. Finally, we design a
fairness utility score for each feature (for feature selection) which
quantifies how this feature influences accurate as well as nondiscriminatory
decisions. Our framework depends on the joint statistics of the data rather
than a particular classifier design. We examine our proposed framework on real
and synthetic data to evaluate its performance.

本篇文章提出了一种基于信息论度量和 Shapley 值函数的公平特征选择框架，以考虑特征与决策结果之间的相关性，量化了不同特征对决策结果准确性与公平性的影响，最终设计了一个特征的公平效用分数，对真实和合成数据进行了性能评估。