FRAPPE: A Group Fairness Framework for Post-Processing Everything
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929391556100096 |
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| author | Tifrea, Alexandru Lahoti, Preethi Packer, Ben Halpern, Yoni Beirami, Ahmad Prost, Flavien |
| author_facet | Tifrea, Alexandru Lahoti, Preethi Packer, Ben Halpern, Yoni Beirami, Ahmad Prost, Flavien |
| contents | Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02592 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FRAPPE: A Group Fairness Framework for Post-Processing Everything Tifrea, Alexandru Lahoti, Preethi Packer, Ben Halpern, Yoni Beirami, Ahmad Prost, Flavien Machine Learning Computers and Society Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels. |
| title | FRAPPE: A Group Fairness Framework for Post-Processing Everything |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2312.02592 |