The use of machine learning (ML) in high-stakes societal decisions has
encouraged the consideration of fairness throughout the ML lifecycle. Although
data integration is one of the primary steps to generate high quality training
data, most of the fairness literature ignores this stage. In this work, we
consider fairness in the integration component of data management, aiming to
identify features that improve prediction without adding any bias to the
dataset. We work under the causal interventional fairness paradigm. Without
requiring the underlying structural causal model a priori, we propose an
approach to identify a sub-collection of features that ensure the fairness of
the dataset by performing conditional independence tests between different
subsets of features. We use group testing to improve the complexity of the
approach. We theoretically prove the correctness of the proposed algorithm to
identify features that ensure interventional fairness and show that sub-linear
conditional independence tests are sufficient to identify these variables. A
detailed empirical evaluation is performed on real-world datasets to
demonstrate the efficacy and efficiency of our technique.

本文介绍了一种使用因果干预公平性范例，通过在数据管理的集成组件中考虑公平性来识别特征以提高预测质量而不添加偏差的方法，提出了一种使用条件独立性检测的方法来确定确保干预公平的特征子集，并通过现实世界的数据集进行了详细的实证评估，证明了方法的有效性和效率。