统计机器学习与因果效应评估系列学术报告（17）

报告题目：Peer-induced Fairness: A Simple Causal Approach for Algorithmic Bias Discovery in Credit Approval

报告人： 陈泽汛 博士 （英国爱丁堡大学）

报告时间：2025年3月24日下午 16:00-17:00

报告地点：学院106室

报告摘要：

In today's world, where AI and automation increasingly shape decision-making processes, ensuring algorithmic fairness is paramount. While much attention has been given to fairness concepts like statistical parity and equal opportunity, practical challenges in detecting and addressing bias remain. Traditional methods often involve embedding fairness metrics into algorithms, which can compromise their accuracy.

In this seminar, I will introduce a fundamental shift in tackling algorithmic bias by presenting our novel "peer-induced fairness" framework. This approach leverages counterfactual fairness and advanced causal inference techniques, including the Single World Intervention Graph, to detect bias at the individual level through peer comparisons and hypothesis testing. Focusing on the context of credit approval, our framework addresses common issues such as data scarcity and imbalance, and operates independently of specific decision-making methodologies, such as classifier selection. It provides explainable feedback to individuals who receive adverse decisions, distinguishing between algorithmic bias, discrimination, and the capabilities of the subjects involved. Our framework has been validated using a dataset of SMEs, demonstrating its effectiveness in identifying unfair practices and suggesting practical interventions. The results show that 'peer-induced fairness' not only improves fairness in algorithmic decisions but also serves as a flexible, transparent, and adaptable tool for diverse applications.

报告人简介：陈泽汛，英国爱丁堡大学商学院助理教授，研究方向包括非参贝叶斯统计，概率化统计学习方法，时间序列分析，算法公平性与信用评级，多维度人口流动模型，机器学习方法在人口流动与城市规划上的应用，高斯过程机器学习在金融期权与市场风险上的应用等等。陈博士2013年本科毕业于山东大学数学与应用数学专业，2017年博士毕业于英国莱斯特大学数学系，研究方向是高斯过程机器学习算法及其推广与应用。博士毕业后曾在英国苏塞克斯大学作为博士后研究员参与英国EPSRC项目，提出高斯过程分类器的公平性改进。之后又在英国埃克塞特大学作为博士后参与英美联合US Army Research的项目，研究内容包括时间，空间与社交数据下人口流动问题的建模等。陈博士曾在各类期刊会议上发表过一系列高水平文章，其中包括Nature Communications，Neural Computation and Application，Measurement等等，目前是英国皇家统计协会会员, 英国皇家统计协会会员青年统计学家分会组委会秘书，英国数学与应该数学协会会员，国际统计协会会员，并担任各类数学统计，量化金融，机器学习，工程机械，人口流动，交通运输方面期刊的审稿人，如IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)，Association for the Advancement of Artificial Intelligence (AAAI)，Transportation Research Part C: Emerging Technologies，The European Journal of Finance， Journal of Forecasting等。