Fair Classification under Covariate Shift and Missing Protected Attribute – an Investigation using Related Features

04/17/2022
by   Manan Singh, et al.
0

This study investigated the problem of fair classification under Covariate Shift and missing protected attribute using a simple approach based on the use of importance-weights to handle covariate-shift and, Related Features arXiv:2104.14537 to handle missing protected attribute.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/07/2019

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Most approaches for ensuring or improving a model's fairness with respec...
research
06/06/2022

Class Prior Estimation under Covariate Shift – no Problem?

We show that in the context of classification the property of source and...
research
07/19/2019

Fair quantile regression

Quantile regression is a tool for learning conditional distributions. In...
research
07/07/2020

README: REpresentation learning by fairness-Aware Disentangling MEthod

Fair representation learning aims to encode invariant representation wit...
research
10/11/2020

Robust Fairness under Covariate Shift

Making predictions that are fair with regard to protected group membersh...
research
04/19/2023

An Offline Metric for the Debiasedness of Click Models

A well-known problem when learning from user clicks are inherent biases ...
research
08/18/2021

Contrastive Identification of Covariate Shift in Image Data

Identifying covariate shift is crucial for making machine learning syste...

Please sign up or login with your details

Forgot password? Click here to reset