【专家简介】：姜蓓博士是阿尔伯塔大学数学与统计系全职教授、加拿大CIFAR 人工智能讲席教授，同时担任阿尔伯塔机器智能研究所（Amii）会士。她的研究方向聚焦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，以及面向复杂异构数据的统计学习。她在统计学与机器学习领域顶级期刊和会议上发表大量论文，包括JASA、AOS、JMLR、NeurIPS、ICML和ICLR。姜博士目前担任Statistics Surveys联合主编、JASA副主编以及 2026 年NeurIPS领域主席。她是 2025 年 COPSS 青年领军学者奖与阿尔伯塔大学理学院科研奖获得者。
【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.
【报告时间】：2026年09月23日（周三）10:30-11:30
【报告地点】：崇真楼110会议室
