1 citations · 1 across the 6 of their papers we have counts for
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Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhanc…
Counterfactual Explanation for Regression via Disentanglement in Latent Space
Xuan Zhao, Klaus Broelemann, Gjergji Kasneci
Counterfactual Explanations (CEs) help address the question: How can the factors that influence the prediction of a predictive model be changed to achieve a more favorable outcome…
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
Xuan Zhao, Simone Fabbrizzi, Paula Reyero Lobo +4
The unequal representation of different groups in a sample population can lead to discrimination of minority groups when machine learning models make automated decisions. To addres…
Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, an…