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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Vydané v:	Proceedings / International Conference on Software Engineering s. 743 - 755
Hlavní autori:	Chen, Zhenpeng, Li, Xinyue, Zhang, Jie M., Sarro, Federica, Liu, Yang
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 26.04.2025
Predmet:	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
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