2026年9月18日（周五）下午2:30，深圳河套学院荣幸地邀请到阿尔伯塔大学数学与统计系的江蓓教授，在B301教室带来主题为“一种用于生成公平合成标签的数据增强预测框架：可控的公平性–忠实性权衡”的学术报告分享。诚挚欢迎学院师生及对该领域感兴趣的产学研界同仁参与讲座。

Title

一种用于生成公平合成标签的数据增强预测框架：可控的公平性–忠实性权衡

A Data-Augmented Predictive Framework for Generating Fair Synthetic Labels with Controlled Fairness–Faithfulness Trade-offs

About the Speaker

江蓓 教授
Prof. Bei Jiang

江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《美国统计协会期刊》《统计学年鉴》《机器学习研究期刊》、神经信息处理系统大会（NeurIPS）、国际机器学习会议（ICML）和国际学习表征会议（ICLR）。江博士目前担任《统计学综述》联合主编、《美国统计协会期刊》副主编以及 2026 年神经信息处理系统大会（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。

Dr. Bei Jiang is a full Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, a Canada CIFAR AI Chair, and a Fellow of the Alberta Machine Intelligence Institute (Amii). Her research focuses on the statistical foundations of trustworthy AI, including data privacy, algorithmic fairness, uncertainty quantification, federated learning, and statistical learning for complex and heterogeneous data. She has published extensively in leading statistics and machine learning venues, including the Journal of the American Statistical Association, Annals of Statistics, Journal of Machine Learning Research, NeurIPS, ICML, and ICLR. Dr. Jiang currently serves as Co-Editor of Statistics Surveys, Associate Editor for the Journal of the American Statistical Association, and Area Chair for NeurIPS 2026. She is a recipient of the 2025 COPSS Emerging Leader Award and the University of Alberta Faculty of Science Research Award.

Abstract

算法决策系统正越来越多地应用于社会敏感领域，由此引发了人们对从历史数据中继承偏见的担忧。公平合成数据生成是一种用于减轻偏见的预处理策略，但现有方法通常依赖黑盒生成模型，这类模型的调参缺乏充分的理论解释。本文提出 fDA，这是一个用于生成公平合成标签的数据增强预测框架。该框架融合两类模型：一类为公平约束模型，用于设定满足目标公平性约束的公平参考条件分布；另一类为保真度保留模型，基于观测标签生成辅助变量，以此留存可控的原始标签信息。合成标签由联合建模上述组件所得到的预测分布采样生成，并配套一套调参机制。针对连续标签，高斯工作设定可将有效噪声水平显式校准至群体不公平性的目标上界。对于连续标签与有序标签，每一组调参值都对应完整定义的预测分布，从而可对公平性与保真度之间的实际权衡进行经验校准。理论层面，当辅助变量具备完全信息时，合成标签在概率与分布意义下收敛于原始标签；当辅助变量不携带任何信息时，该机制退化为公平参考分布。在模拟数据集与真实数据集上开展的实验表明，相较于基于生成对抗网络（GAN）的基线方法，该框架的权衡关系具备可解释性，且保真度更高。

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.

Host

荆炳义 教授

Prof. Bingyi Jing

Date & Time

2026年9月18日（星期五）

下午2:30-4:00

September 18, 2026, Friday,

2:30pm-4:00pm

Venue

深圳河套学院B301教室

（深圳市福田区福保街道红棉路6号，地图导航“深圳河套学院-南门”）

Room B301, Shenzhen Loop Area Institute

(6 Hongmian Rd, Fubao Sub-Street, Futian District, Shenzhen, navigate to "Shenzhen Loop Area Institute (South Gate)" on the map.)

Online link

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腾讯会议号：948-1135-4043

Tencent Meeting: 948-1135-4043