3 papers
cs.LG2025
Explainable post-training bias mitigation with distribution-based fairness metrics
Ryan Franks, Alexey Miroshnikov, Konstandinos Kotsiopoulos
We develop a novel bias mitigation framework with distribution-based fairness constraints suitable for producing demographically blind and explainable machine-learning models acros…
cs.LG2024
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings
Ashkan Golgoon, Ryan Franks, Khashayar Filom +1
In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabu…
cs.LG2021
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a…