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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 743 - 755
Hauptverfasser:	Chen, Zhenpeng, Li, Xinyue, Zhang, Jie M., Sarro, Federica, Liu, Yang
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.04.2025
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!