On the relation between accuracy and fairness in binary classification
Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationClassificationFairnessGeneral ClassificationRelationSimilar Papers 제목 키워드 기반
A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks
The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigation in binary classification tasks, the is…
Multi-class ClassificationBinary ClassificationPenalizing Unfairness in Binary Classification
We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to a…
Binary ClassificationClassificationFairnessGeneral ClassificationOptimal Fairness under Local Differential Privacy
We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first derive a closed-form optimal mechanism fo…
An Accounting Identity for Algorithmic Fairness
We derive an accounting identity for predictive models that links accuracy with common fairness criteria. The identity shows that for globally calibrated models, the weighted sums of miscalibration within groups and erro…
A Stochastic Optimization Framework for Fair Risk Minimization
Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochas…
Binary ClassificationFairnessStochastic Optimization