狗熊会学术讲演 | 吕文斌教授：锻炼和测试机器学习决策中的 ...

英文摘要

Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed non-sensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and a real-world application in loan assessment.

嘉宾引见

吕文斌，北卡罗莱纳州立大学统计学教授。2003年取得哥伦比亚大学统计学博士学位，研究兴味包含精准医疗，高维数据剖析，机器学习，因果推断和网络数据。在各类统计学期刊发表学术成果100余篇，其中包含Biometrika，Journal of the American Statistical Association，Journal of the Royal Statistical Society (Series B)，Annals of Statistics和Journal of Machine Learning Research。已培育了26名博士。研讨项目取得美国国立卫生研讨院多项基金部分资助。吕教授目前担任杂志Biostatistics，Biometrics和Statistica Sinica的副主编，同时是美国统计学会Fellow。

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