FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
This work addresses fairness and explainability drifts for high-stakes ML applications, offering an incremental improvement by combining existing concepts into a novel framework.
The paper tackles the problem of drifts in fairness metrics over time in high-stakes machine learning deployments by proposing FEAMOE, a mixture-of-experts framework that learns fairer and more explainable models while adapting to drifts in accuracy and fairness. Experiments on multiple datasets, including HMDA, show that FEAMOE performs comparably to neural networks in accuracy while maintaining fairness across three measures and handling drifts effectively.
Three key properties that are desired of trustworthy machine learning models deployed in high-stakes environments are fairness, explainability, and an ability to account for various kinds of "drift". While drifts in model accuracy, for example due to covariate shift, have been widely investigated, drifts in fairness metrics over time remain largely unexplored. In this paper, we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at learning fairer, more explainable/interpretable models that can also rapidly adjust to drifts in both the accuracy and the fairness of a classifier. We illustrate our framework for three popular fairness measures and demonstrate how drift can be handled with respect to these fairness constraints. Experiments on multiple datasets show that our framework as applied to a mixture of linear experts is able to perform comparably to neural networks in terms of accuracy while producing fairer models. We then use the large-scale HMDA dataset and show that while various models trained on HMDA demonstrate drift with respect to both accuracy and fairness, FEAMOE can ably handle these drifts with respect to all the considered fairness measures and maintain model accuracy as well. We also prove that the proposed framework allows for producing fast Shapley value explanations, which makes computationally efficient feature attribution based explanations of model decisions readily available via FEAMOE.