Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics. - Alexey Miroshnikov; Konstandinos Kotsiopoulos; Ryan Franks; Arjun Ravi Kannan (CoRR, 2021)

Wasserstein-based fairness interpretability framework for machine learning models. - Alexey Miroshnikov; Konstandinos Kotsiopoulos; Ryan Franks; Arjun Ravi Kannan (Mach. Learn., 2022)

Explainable post-training bias mitigation with distribution-based fairness metrics. - Ryan Franks; Alexey Miroshnikov (CoRR, 2025)

MbExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings. - Ashkan Golgoon; Ryan Franks; Khashayar Filom; Arjun Ravi Kannan (Mach. Learn., 2025)