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Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination

Hort, M; Sarro, F; (2021) Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination. In: Proceedings of the Automated Software Engineering 2021. ASE: Online. Green open access

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Abstract

Machine Learning is a vital part of various modern day decision making software. At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes.

Type: Proceedings paper
Title: Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination
Event: Automated Software Engineering 2021
Open access status: An open access version is available from UCL Discovery
Publisher version: https://conf.researchr.org/details/ase-2021/ase-20...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10133603
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