SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913223430635520 |
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| author | Wang, Zhihao Xie, Yiqun Li, Zhili Jia, Xiaowei Jiang, Zhe Jia, Aolin Xu, Shuo |
| author_facet | Wang, Zhihao Xie, Yiqun Li, Zhili Jia, Xiaowei Jiang, Zhe Jia, Aolin Xu, Shuo |
| contents | Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due to the change in distribution over different regions. While techniques have been developed to improve the transferability of fairness, a solution to the problem is not always feasible with no samples from the new regions, which is a bottleneck for pure data-driven attempts. Fortunately, physics-based mechanistic models have been studied for many problems with major social impacts. We propose SimFair, a physics-guided fairness-aware learning framework, which bridges the data limitation by integrating physical-rule-based simulation and inverse modeling into the training design. Using temperature prediction as an example, we demonstrate the effectiveness of the proposed SimFair in fairness preservation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models Wang, Zhihao Xie, Yiqun Li, Zhili Jia, Xiaowei Jiang, Zhe Jia, Aolin Xu, Shuo Machine Learning Artificial Intelligence Computers and Society Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due to the change in distribution over different regions. While techniques have been developed to improve the transferability of fairness, a solution to the problem is not always feasible with no samples from the new regions, which is a bottleneck for pure data-driven attempts. Fortunately, physics-based mechanistic models have been studied for many problems with major social impacts. We propose SimFair, a physics-guided fairness-aware learning framework, which bridges the data limitation by integrating physical-rule-based simulation and inverse modeling into the training design. Using temperature prediction as an example, we demonstrate the effectiveness of the proposed SimFair in fairness preservation. |
| title | SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2401.15270 |