Non-Invasive Fairness in Learning Through the Lens of Data Drift

Machine Learning (ML) models are widely employed to drive many modern data systems. While they are undeniably powerful tools, ML models often demonstrate imbalanced performance and unfair behaviors. The root of this problem often lies in the fact that different subpopulations commonly display divergent trends: as a learning algorithm tries to identify trends in the data, it naturally favors the trends of the majority groups, leading to a model that performs poorly and unfairly for minority populations. Our goal is to improve the fairness and trustworthiness of ML models by applying only non-invasive interventions, i.e., without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift, which indicates the poor conformance between parts of the data and the trained model.

We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data. Both our methods introduce novel ways to employ the recently-proposed data profiling primitive of Conformance Constraints. Our splitting approach is based on a simple data drift strategy: training separate models for different populations. Our DIFFAIR algorithm enhances this simple strategy by employing conformance constraints, learned over the data partitions, to select the appropriate model to use for predictions on each serving tuple. However, the performance of such a multi-model strategy can degrade severely under poor representation of some groups in the data. We thus propose a single-model, reweighing strategy, CONFAIR, to overcome this limitation. CONFAIR employs conformance constraints in a novel way to derive weights for training data, which are then used to build a single model. Our experimental evaluation over 7 realworld datasets shows that both DIFFAIR and CONFAIR improve the fairness of ML models. We demonstrate scenarios where DIFFAIR has an edge, though CONFAIR has the greatest practical impact and outperforms other baselines. Moreover, as a modelagnostic technique, CONFAIR stays robust when used against different models than the ones on which the weights have been learned, which is not the case for other states of the art.