Penalizing 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 achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.
Code (2)
Tasks
Binary ClassificationClassificationFairnessGeneral ClassificationSimilar Papers 제목 키워드 기반
Optimal 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…
Fairness and Unfairness in Binary and Multiclass Classification: Quantifying, Calculating, and Bounding
We propose a new interpretable measure of unfairness, that allows providing a quantitative analysis of classifier fairness, beyond a dichotomous fair/unfair distinction. We show how this measure can be calculated when th…
FairnessMetrizing Fairness
We study supervised learning problems that have significant effects on individuals from two demographic groups, and we seek predictors that are fair with respect to a group fairness criterion such as statistical parity (…
FairnessAn 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 Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups. Properly powering the statistical analysis of such audits is crucial for obtaining inf…
Binary ClassificationFairness