CUED Publications database

One-network adversarial fairness

Adel, T and Valera, I and Ghahramani, Z and Weller, A (2018) One-network adversarial fairness. In: AAAI 2019, -- to -- pp. 2412-2420..

Full text not available from this repository.

Abstract

© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. There is currently a great expansion of the impact of machine learning algorithms on our lives, prompting the need for objectives other than pure performance, including fairness. Fairness here means that the outcome of an automated decision-making system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy. Our framework provides one way to quantify the tradeoff between fairness and accuracy, while also leading to strong empirical performance.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Unnamed user with email sms67@cam.ac.uk
Date Deposited: 01 May 2020 21:54
Last Modified: 09 Sep 2021 02:13
DOI: