A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911530332717056 |
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| author | Zhao, Zhouting Ng, Tin Lok James |
| author_facet | Zhao, Zhouting Ng, Tin Lok James |
| contents | Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18838 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction Zhao, Zhouting Ng, Tin Lok James Machine Learning Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy. |
| title | A Model Ensemble-Based Post-Processing Framework for Fairness-Aware Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.18838 |