Hierarchical Bayesian models to mitigate systematic disparities in prediction with proxy outcomes
提出分层贝叶斯测量模型，解决因代理标签与真实结果差异导致的预测系统性偏差，提升准确性和算法公平性，并允许在信息有限时评估预测敏感性。
Abstract Label bias occurs when the outcome of interest is not directly observable and instead, modelling is performed with proxy labels. When the difference between the true outcome and the proxy label is correlated with predictors, this can yield systematic disparities in predictions for different groups of interest. We propose Bayesian hierarchical measurement models to address these issues. When strong prior information about the measurement process is available, our approach improves accuracy and helps with algorithmic fairness. If prior knowledge is limited, our approach allows assessment of the sensitivity of predictions to the unknown specifications of the measurement process. This can help practitioners gauge if enough substantive information is available to guarantee the desired accuracy and avoid disparate predictions when using proxy outcomes. We demonstrate our approach through practical examples.