On the Maximal Local Disparity of Fairness-Aware Classifiers
Fairness has become a crucial aspect in the development of trustworthy machine learning algorithms. Current fairness metrics to measure the violation of demographic parity have the following drawbacks: (i) the average difference of model predictions on two groups cannot reflect their distribution disparity, and (ii) the overall calculation along all possible predictions conceals the extreme local disparity at or around certain predictions. In this work, we propose a novel fairness metric called Maximal Cumulative ratio Disparity along varying Predictions' neighborhood (MCDP), for measuring the maximal local disparity of the fairness-aware classifiers. To accurately and efficiently calculate the MCDP, we develop a provably exact and an approximate calculation algorithm that greatly reduces the computational complexity with low estimation error. We further propose a bi-level optimization algorithm using a differentiable approximation of the MCDP for improving the algorithmic fairness. Extensive experiments on both tabular and image datasets validate that our fair training algorithm can achieve superior fairness-accuracy trade-offs.
Code (1)
Tasks
FairnessSimilar Papers 제목 키워드 기반
Pareto Optimality in No-Harm Fairness
Common fairness definitions in machine learning focus on balancing various notions of disparity and utility. In this work we study fairness in the context of risk disparity among sub-populations. We introduce the framewo…
FairnessBayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairne…
AttributeClassificationFairnessLEMMAFedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
With emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e.g., female, male). Current research predomi…
FairnessFederated LearningMulti-class ClassificationMinimax Optimal Fair Classification with Bounded Demographic Disparity
Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \emph{finite dataset} -- as opposed to th…
Binary ClassificationClassificationFairnessDemystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc. Existing works often focus on either $\text…
FairnessFederated Learning