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Exploring the Impact of Lay User Feedback for Improving AI Fairness
Fairness in AI is a growing concern for high-stakes decision making.
Fair Oversampling Technique using Heterogeneous Clusters
Class imbalance and group (e. g., race, gender, and age) imbalance are acknowledged as two reasons in data that hinder the trade-off between fairness and utility of machine learning classifiers.
A Pre-processing Method for Fairness in Ranking
As far as the fairness measurements in ranking are represented as a linear constraint of the ranking models, we proved that the minimization of loss function subject to the constraints is reduced to the closed solution of the minimization problem augmented by weights to training data.
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