Towards fairness-aware multi-objective optimization
Abstract 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 commo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01668-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571188193263616 |
|---|---|
| author | Guo Yu Lianbo Ma Xilu Wang Wei Du Wenli Du Yaochu Jin |
| author_facet | Guo Yu Lianbo Ma Xilu Wang Wei Du Wenli Du Yaochu Jin |
| author_sort | Guo Yu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a153ae3ad2b64f979b7663f35d2b4f8c |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-a153ae3ad2b64f979b7663f35d2b4f8c2025-02-02T12:49:55ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111112010.1007/s40747-024-01668-wTowards fairness-aware multi-objective optimizationGuo Yu0Lianbo Ma1Xilu Wang2Wei Du3Wenli Du4Yaochu Jin5Institute of Intelligent Manufacturing, Nanjing Tech UniversitySoftware College, Northeastern UniversityFaculty of Technology, Bielefeld UniversityKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and TechnologyKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and TechnologySchool of Engineering, Westlake UniversityAbstract 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.https://doi.org/10.1007/s40747-024-01668-wFairness-aware multi-objective optimizationPreferenceFairness-aware machine learningData-driven optimizationFederated optimization |
| spellingShingle | Guo Yu Lianbo Ma Xilu Wang Wei Du Wenli Du Yaochu Jin Towards fairness-aware multi-objective optimization Complex & Intelligent Systems Fairness-aware multi-objective optimization Preference Fairness-aware machine learning Data-driven optimization Federated optimization |
| title | Towards fairness-aware multi-objective optimization |
| title_full | Towards fairness-aware multi-objective optimization |
| title_fullStr | Towards fairness-aware multi-objective optimization |
| title_full_unstemmed | Towards fairness-aware multi-objective optimization |
| title_short | Towards fairness-aware multi-objective optimization |
| title_sort | towards fairness aware multi objective optimization |
| topic | Fairness-aware multi-objective optimization Preference Fairness-aware machine learning Data-driven optimization Federated optimization |
| url | https://doi.org/10.1007/s40747-024-01668-w |
| work_keys_str_mv | AT guoyu towardsfairnessawaremultiobjectiveoptimization AT lianboma towardsfairnessawaremultiobjectiveoptimization AT xiluwang towardsfairnessawaremultiobjectiveoptimization AT weidu towardsfairnessawaremultiobjectiveoptimization AT wenlidu towardsfairnessawaremultiobjectiveoptimization AT yaochujin towardsfairnessawaremultiobjectiveoptimization |