7 citations · 10 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
The Initial Screening Order Problem
Jose M. Alvarez, Antonio Mastropietro, Salvatore Ruggieri
We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting $k…
Beyond Demographic Parity: Redefining Equal Treatment
Carlos Mougan, Laura State, Antonio Ferrara +2
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics. Related work in machine learning h…
Domain Adaptive Decision Trees: Implications for Accuracy and Fairness
Jose M. Alvarez, Kristen M. Scott, Salvatore Ruggieri +1
In uses of pre-trained machine learning models, it is a known issue that the target population in which the model is being deployed may not have been reflected in the source popula…