让机器学习预测更公平，自动识别潜在偏见群体并保证覆盖
Fair Conformal Classification via Learning Representation-Based Groups
- 通过学习特征组合自动划分群体，动态构建公平预测集
- 在真实和合成数据上实现对不同群体的均衡覆盖率
- 适合关注模型公平性与可信推理的研究者和工程师
共形预测方法为机器学习模型提供了严格的边际覆盖保证，但无法考虑算法偏见，削弱了公平性与可信度。本文提出一种分类任务下的公平共形推断框架，通过非线性特征组合自适应识别子群体，构建在这些群体上具有条件覆盖保证的预测集。该方法在生成紧凑、信息丰富预测集的同时，确保对不公平对待群体实现自适应的等量覆盖，为可信机器学习提供可行路径。在合成与真实世界数据集上的大量实验验证了该框架的有效性。
原文摘要 · Abstract (English)
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.
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