WebUsing the causal graph describing the data and the anticipated shifts, we specify an approach based on feature selection that exploits conditional independencies in the data to estimate accuracy and fairness metrics for the test set. We show that for specific fairness definitions, the resulting model satisfies a form of worst-case optimality. WebWe seek fair decisions under these assumptions on target data with unknown labels.We propose an approach that obtains the predictor that is robust to the worst-case in terms …

Fairness Transferability Subject to Bounded Distribution Shift

WebRobust Fairness under Covariate Shift Ashkan Rezaei1, Anqui Liu2, Omid Memarrast1, Brian Ziebart1 1 Departmentof Computer Science, University of Illinois at Chicago 2 California Institute of Technology [email protected], [email protected], [email protected], [email protected] Abstract Making predictions that are fair with regard to protected WebMay 18, 2024 · We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label … creative t3130 inspire 2.1 speakers

Fairness without Demographics through Adversarially …

WebOct 11, 2024 · We seek fair decisions under these assumptions on target data with unknown labels.We propose an approach that obtains the predictor that is robust to the worst-case in terms of target... Webunder data shift, the covariate shift assumption holds if we use only the separating set of features for prediction. Our model builds on robust classification method of (Liu and … WebWe investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. creative t40 sii speakers system