02/02/2021

Robust Fairness Under Covariate Shift

Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian D. Ziebart

Keywords:

Abstract Paper Similar Papers

Abstract: Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We 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. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38949095
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 8:12 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 19:30 
 0
 0
 0
 0
 3:12 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 17:27 
 0
 0
 0
 0
 10:29 
 0
 0
 0
 0
 13:34 
 0
 0
 0
 0
 11:05 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 6:36 
 0
 0
 0
 0
 7:04 
 0
 0
 0
 0
 12:42 
 0
 0
 0
 0
 13:44 
 0
 0
 0
 0
 12:27 
 0
 0
 0
 0
 13:41 
 0
 0
 0
 1
 5:39 
 0
 0
 0
 0
 15:58 
 0
 0
 0
 0
 9:53 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 15:17 
 0
 0
 0
 0
 15:03 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 17:53 
 0
 0
 0
 0
 5:12 
 0
 0
 0
 0
 14:24 
 0
 0
 0
 1
 14:57 
 0
 0
 0
 0
 5:56 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 4:41 
 0
 0
 0
 0
 12:23 
 0
 0
 0
 0
 11:12 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 12:55 
 0
 0
 0
 0
 14:11 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 14:57 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 10:03 
 0
 0
 0
 0
 12:10