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.
翻译:与准确决定相关的特征可能导致对非特权群体,例如某些种族或性别群体的明确或隐含形式的歧视。这主要是由于培训数据中存在偏见,而培训数据往往被复制,甚至因学习算法而加剧。在数据一级查明和衡量这些偏见是一个具有挑战性的问题,因为特征和决定结果之间存在相互依存关系。在这项工作中,我们制定了公平认知特征选择框架,其中考虑到特征和决定结果之间的相互关系,并基于对特征的准确性和歧视性影响的信息理论性措施。特别是,我们首先提出信息理论性措施,量化不同特征对决策结果的准确性和歧视性的影响。我们然后用沙普利价值函数来推断每个特征的边际影响;在合作游戏理论中所使用的一种解决方案概念,用以估计玩家在联盟游戏中的边际贡献。最后,我们为每个特征(特征选择)设计一个公平效用评分,用以量化这些特征的准确性和歧视性影响我们的拟议数据框架如何影响其准确性、而不是合成数据框架的精确性能。我们先研究其拟议的数据的准确性能如何取决于我们提出的数据是如何以非歧视性性化的。