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Found 3 papers, 2 papers with code
Procedural Fairness in Machine Learning
We propose a novel metric to evaluate the group procedural fairness of ML models, called $GPF_{FAE}$, which utilizes a widely used explainable artificial intelligence technique, namely feature attribution explanation (FAE), to capture the decision process of the ML models.
EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect
Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy.
Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
A performance selector is designed to switch the search dynamically and automatically between the global and local search stages.
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