Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software

Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML...

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Bibliographic Details

Published in:	Proceedings / International Conference on Software Engineering pp. 743 - 755
Main Authors:	Chen, Zhenpeng, Li, Xinyue, Zhang, Jie M., Sarro, Federica, Liu, Yang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26.04.2025
Subjects:	Decision making Ensemble learning input mutation intersectional fairness Machine learning Measurement output ensemble Predictive models Prevention and mitigation Propulsion Social sciences Software Software engineering Training
ISSN:	1558-1225
Online Access:	Get full text
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