Paper Reading AI Learner

Adversarial Learned Fair Representations using Dampening and Stacking

2022-03-16 14:07:36
Max Knobbout

Abstract

As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. This paper builds upon this work by introducing a novel algorithm which uses dampening and stacking to learn adversarial fair representations. Results show that that our algorithm improves upon earlier work in both censoring and reconstruction.

Abstract (translated)

URL

https://arxiv.org/abs/2203.08637

PDF

https://arxiv.org/pdf/2203.08637.pdf


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