贝叶斯多臂老虎机方法：在线挑选兼顾准确率与公平性的分类器
Bayesian Learning to Select Fair Classifiers
把挑分类器当成多臂老虎机来玩：准确率和公平性都计入奖励，标签晚到也能用，实测遗憾值明显低于基线。
一篇 arXiv 论文将在线挑选分类器的问题建模为贝叶斯多臂老虎机，用 Thompson Sampling 在预测准确率与敏感属性的子群体公平性之间做权衡。每个候选标注者被当作一个臂，通过后验采样决定下一个查询对象，并支持真实类别标签延迟到达。论文在多组真实数据集上验证，覆盖表格数据分类器、大语言模型和人工标注者三类来源，相对基线方法取得显著的遗憾值下降。理论部分给出了同时覆盖准确率与公平性两个维度的性能保证。
Bayesian Learning to Select Fair Classifiers
Evaluations of classifiers (AI or human or agents) often assume a batch setting in which every agent produces labels on every example in a test set, which can be expensive or impractical in many contexts. This motivates online policies that adaptively select agents to query as examples arrive and update policies as outcomes are observed. We study the problem of selecting, in an online stream, the agent that best balances (i) predictive performance and (ii) subgroup fairness of a sensitive attribute, and that allows for delayed observations of true class labels. We formulate this as a Bayesian multi-armed bandit problem using Thompson Sampling, explicitly balancing accuracy and fairness. We conduct empirical evaluations on real-world datasets spanning problems involving tabular classifiers, large language models, and human labelers, demonstrating substantial reductions in regret relative to baselines. Our theoretical contributions include guarantees on the performance of the selected agent that incorporate both the accuracy and fairness aspects of the problem.
