报告人：姜蓓 教授

报告题目：A Data-Augmented Predictive Framework for Generating Fair Synthetic Labels with Controlled Fairness-Faithfulness Trade-offs

时间：2026年9月21日（周一）  10:00-11:00

地点：西安交通大学兴庆校区数学楼2-2会议室

报告摘要：

Algorithmic decision systems are increasingly used in socially sensitive domains, raising concerns about bias inherited from historical data. Fair synthetic data generation provides a pre-processing strategy for bias mitigation, but existing methods often rely on black-box generative models whose tuning parameters have limited theoretical interpretation. We propose fDA, a Data-Augmented predictive framework for generating Fair synthetic labels. The framework combines a fairness-enforcing model, which specifies a fair reference conditional law satisfying the desired fairness constraint, with a faithfulness-preserving model, which generates auxiliary variables from observed labels to retain controlled original-label information. Synthetic labels are sampled from the predictive distribution induced by jointly modeling these components, coupled with a tuning mechanism. For continuous labels, the Gaussian working specification yields explicit calibration of the effective noise level to a target population upper bound on unfairness. For both continuous and ordinal labels, the predictive distribution is fully specified for each tuning value, enabling empirical calibration of the achieved fairness--faithfulness trade-off. Theoretically, when the auxiliary variable becomes fully informative, synthetic labels converge to the original labels in probability and distribution; when it becomes non-informative, the mechanism reduces to the fair reference law. Experiments on simulated and real datasets show interpretable trade-offs and improved faithfulness over GAN-based baselines.

报告人简介：

姜蓓是加拿大阿尔伯塔大学数学与统计科学系教授，加拿大 CIFAR 人工智能主席，阿尔伯塔机器智能研究所（Amii）会士，荣获 2025 年“ COPSS 新兴领军学者奖”。她的研究方向聚焦于可信人工智能的统计基础，涵盖数据隐私、算法公平性、不确定性量化、联邦学习，以及面向复杂异质数据的统计学习。她在统计学与机器学习顶刊、顶会发表诸多学术成果，包括JASA、AOS、JMLR，以及 NeurIPS、ICML、ICLR 等国际顶级会议。姜蓓教授现任《Statistics Surveys》联合主编，JASA 副主编，并担任 2026 年 NeurIPS 、ICML和ICLR的领域主席。