提出可解释公平性框架，让预测置信区间对不同群体更公平
A Generic Framework for Conformal Fairness
- 基于交换性假设设计公平性校准方法
- 在图与表格数据上有效缩小不同群体的覆盖率差距
- 适合关注算法公平性的研究者与实践者
置信预测（Conformal Prediction, CP）是一种流行的机器学习不确定性量化方法。尽管CP能保证真实标签的覆盖概率，但其保障不考虑数据中敏感属性的存在。本文首次形式化定义了‘置信公平性’（Conformal Fairness），并提出一个理论完备的算法与框架，以控制不同敏感群体间覆盖率的差异。该框架利用CP隐含的交换性假设，而非传统独立同分布（IID）假设，因此可适用于非IID数据类型，如图数据。在图数据与表格数据上的实验表明，该算法不仅能有效控制公平性相关的覆盖率差距，且实际表现符合理论预期。
原文摘要 · Abstract (English)
Conformal Prediction (CP) is a popular method for uncertainty quantification with machine learning models. While conformal prediction provides probabilistic guarantees regarding the coverage of the true label, these guarantees are agnostic to the presence of sensitive attributes within the dataset. In this work, we formalize \textit{Conformal Fairness}, a notion of fairness using conformal predictors, and provide a theoretically well-founded algorithm and associated framework to control for the gaps in coverage between different sensitive groups. Our framework leverages the exchangeability assumption (implicit to CP) rather than the typical IID assumption, allowing us to apply the notion of Conformal Fairness to data types and tasks that are not IID, such as graph data. Experiments were conducted on graph and tabular datasets to demonstrate that the algorithm can control fairness-related gaps in addition to coverage aligned with theoretical expectations.
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