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Found 5 papers, 5 papers with code
Minimax Optimal Fair Classification with Bounded Demographic Disparity
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness.
FairRR: Pre-Processing for Group Fairness through Randomized Response
The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems.
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints.
Fair Bayes-Optimal Classifiers Under Predictive Parity
This paper considers predictive parity, which requires equalizing the probability of success given a positive prediction among different protected groups.
Bayes-Optimal Classifiers under Group Fairness
Machine learning algorithms are becoming integrated into more and more high-stakes decision-making processes, such as in social welfare issues.
