Towards fairness-aware multi-objective optimization Article Swipe
Guo Yu
,
Lianbo Ma
,
Xilu Wang
,
Wei Du
,
Wenli Du
,
Yaochu Jin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-024-01668-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-024-01668-w
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.
Related Topics
Concepts
Computer science
Multi-objective optimization
Fairness measure
Optimization problem
Context (archaeology)
Perspective (graphical)
Resource allocation
Computational intelligence
Management science
Mathematical optimization
Artificial intelligence
Machine learning
Mathematics
Economics
Algorithm
Wireless
Biology
Throughput
Computer network
Paleontology
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-024-01668-w
- OA Status
- gold
- Cited By
- 9
- References
- 172
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404544918
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404544918Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s40747-024-01668-wDigital Object Identifier
- Title
-
Towards fairness-aware multi-objective optimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-20Full publication date if available
- Authors
-
Guo Yu, Lianbo Ma, Xilu Wang, Wei Du, Wenli Du, Yaochu JinList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-024-01668-wPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1007/s40747-024-01668-wDirect OA link when available
- Concepts
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Computer science, Multi-objective optimization, Fairness measure, Optimization problem, Context (archaeology), Perspective (graphical), Resource allocation, Computational intelligence, Management science, Mathematical optimization, Artificial intelligence, Machine learning, Mathematics, Economics, Algorithm, Wireless, Biology, Throughput, Computer network, Paleontology, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
172Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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