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Fair Multiple Decision Making Through Soft Interventions
Yaowei Hu · Yongkai Wu · Lu Zhang · Xintao Wu

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #863

Previous research in fair classification mostly focuses on a single decision model. In reality, there usually exist multiple decision models within a system and all of which may contain a certain amount of discrimination. Such realistic scenarios introduce new challenges to fair classification: since discrimination may be transmitted from upstream models to downstream models, building decision models separately without taking upstream models into consideration cannot guarantee to achieve fairness. In this paper, we propose an approach that learns multiple classifiers and achieves fairness for all of them simultaneously, by treating each decision model as a soft intervention and inferring the post-intervention distributions to formulate the loss function as well as the fairness constraints. We adopt surrogate functions to smooth the loss function and constraints, and theoretically show that the excess risk of the proposed loss function can be bounded in a form that is the same as that for traditional surrogated loss functions. Experiments using both synthetic and real-world datasets show the effectiveness of our approach.

Author Information

Yaowei Hu (University of Arkansas)
Yongkai Wu (Clemson University)
Lu Zhang (University of Arkansas)
Xintao Wu (University of Arkansas)

Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at University of Arkansas.

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