  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.
