BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity

12/17/2018
by   Alexey A. Gritsenko, et al.
0

We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demographic parity with respect to the sensitive information. The net result is that these BriarPatches provide an intervention mechanism available at user level, and complements prior research on fair representations that were previously only applicable by model developers and ML experts.

READ FULL TEXT
research
06/06/2019

Flexibly Fair Representation Learning by Disentanglement

We consider the problem of learning representations that achieve group a...
research
06/15/2020

Learning Smooth and Fair Representations

Organizations that own data face increasing legal liability for its disc...
research
06/23/2022

Minimax Optimal Fair Regression under Linear Model

We investigate the minimax optimal error of a fair regression problem un...
research
02/26/2020

Fair Learning with Private Demographic Data

Sensitive attributes such as race are rarely available to learners in re...
research
01/04/2023

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Mitigating the discrimination of machine learning models has gained incr...
research
08/20/2018

Adversarial Removal of Demographic Attributes from Text Data

Recent advances in Representation Learning and Adversarial Training seem...
research
01/11/2021

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Controlling bias in training datasets is vital for ensuring equal treatm...

Please sign up or login with your details

Forgot password? Click here to reset