FairSense: Long-Term Fairness Analysis of ML-Enabled Systems

Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fa...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 782 - 794
Hauptverfasser:	She, Yining, Biswas, Sumon, Kastner, Christian, Kang, Eunsuk
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.04.2025
Schlagworte:	Environmental factors Feedback loop long-term fairness Machine learning algorithms ml-enabled system Monte Carlo methods Opioids Prediction algorithms Prevention and mitigation Sensitivity analysis simulation-based analysis Software engineering system design Testing
ISSN:	1558-1225
Online-Zugang:	Volltext
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