Optimising Equal Opportunity Fairness in Model Training

Anonymous

Paper Link: https://openreview.net/forum?id=mtVeNh_4dh8

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Aili Shen

Copyright Consent Name And Address: The University of Melbourne, Parkville VIC 3010, Australia