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Towards fairness-aware multi-objective optimization
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DOI：10.1007/s40747-024-01668-w.png)
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Abstract
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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization. Subsequently, we explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multi-objective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a solid step forward towards understanding fairness in the context of optimization. Additionally, we aim to promote research interests in fairness-aware multi-objective optimization.
Keywords:
Fairness-aware multi-objective optimization
Preference
Fairness-aware machine learning
Data-driven optimization
Federated optimization
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