Related papers: Optimising Equal Opportunity Fairness in Model Training

Optimising Equal Opportunity Fairness in Model Training

URL: http://arxiv.org/abs/2205.02393v1
Date: Thu, 5 May 2022 01:57:58 GMT
Title: Optimising Equal Opportunity Fairness in Model Training
Authors: Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin, Lea Frermann
Abstract summary: Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. We propose two novel training objectives which directly optimise for the widely-used criterion of it equal opportunity, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.
Score: 60.0947291284978
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing societal preconceptions. Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. However, a disconnect between fairness criteria and training objectives makes it difficult to reason theoretically about the effectiveness of different techniques. In this work, we propose two novel training objectives which directly optimise for the widely-used criterion of {\it equal opportunity}, and show that they are effective in reducing bias while maintaining high performance over two classification tasks.

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