HIFI: Explaining and Mitigating Algorithmic Bias Through the Lens of Game-Theoretic Interactions

Machine Learning (ML) algorithms are increasingly used in decision-making process across various social-critical domains, but they often somewhat inherit and amplify bias from their training data, leading to unfair and unethical outcomes. This issue highlights the urgent need for effective methods t...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 756 - 768
Hauptverfasser:	Zhang, Lingfeng, Wang, Zhaohui, Zhang, Yueling, Zhang, Min, Wang, Jiangtao
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.04.2025
Schlagworte:	algorithmic bias bias mitigation Decision making explainable artificial intelligence fairness game-theoretic interaction Lenses Machine learning Machine learning algorithms Mathematical models Measurement Prevention and mitigation Software algorithms Software engineering Training data
ISSN:	1558-1225
Online-Zugang:	Volltext
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