Fair Conformal Classification via Learning Representation-Based Groups
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866916005363580928 |
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| author | Xu, Senrong Zhou, Yanke Tan, Yuhao Li, Zenan Yao, Yuan Chen, Taolue Xu, Feng Ma, Xiaoxing |
| author_facet | Xu, Senrong Zhou, Yanke Tan, Yuhao Li, Zenan Yao, Yuan Chen, Taolue Xu, Feng Ma, Xiaoxing |
| contents | Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, thereby undermining fairness and trust. This paper introduces a fair conformal inference framework for classification tasks. The proposed method constructs prediction sets that guarantee conditional coverage on adaptively identified subgroups, which can be implicitly defined through nonlinear feature combinations. By balancing effectiveness and efficiency in producing compact, informative prediction sets and ensuring adaptive equalized coverage across unfairly treated subgroups, our approach paves a practical pathway toward trustworthy machine learning. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12195 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fair Conformal Classification via Learning Representation-Based Groups Xu, Senrong Zhou, Yanke Tan, Yuhao Li, Zenan Yao, Yuan Chen, Taolue Xu, Feng Ma, Xiaoxing Machine Learning Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to account for algorithmic biases, thereby undermining fairness and trust. This paper introduces a fair conformal inference framework for classification tasks. The proposed method constructs prediction sets that guarantee conditional coverage on adaptively identified subgroups, which can be implicitly defined through nonlinear feature combinations. By balancing effectiveness and efficiency in producing compact, informative prediction sets and ensuring adaptive equalized coverage across unfairly treated subgroups, our approach paves a practical pathway toward trustworthy machine learning. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the framework. |
| title | Fair Conformal Classification via Learning Representation-Based Groups |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.12195 |