Adversarial Learned Fair Representations using Dampening and Stacking

Autor:	Knobbout, Max
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computers and Society 68T07 I.2.6
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.08637 Zobrazit plný text záznamu View this record from Arxiv