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This content will become publicly available on April 25, 2023

Title: Fairness Guarantees under Demographic Shift
Recent studies found that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To address this challenge, recent machine learning algorithms have been designed to limit the likelihood such unfair behavior occurs. However, these approaches typically assume the data used for training is representative of what will be encountered in deployment, which is often untrue. In particular, if certain subgroups of the population become more or less probable in deployment (a phenomenon we call demographic shift), prior work's fairness assurances are often invalid. In this paper, we consider the impact of demographic shift and present a class of algorithms, called Shifty algorithms, that provide high-confidence behavioral guarantees that hold under demographic shift when data from the deployment environment is unavailable during training. Shifty, the first technique of its kind, demonstrates an effective strategy for designing algorithms to overcome demographic shift's challenges. We evaluate Shifty using the UCI Adult Census dataset, as well as a real-world dataset of university entrance exams and subsequent student success. We show that the learned models avoid bias under demographic shift, unlike existing methods. Our experiments demonstrate that our algorithm's high-confidence fairness guarantees more » are valid in practice and that our algorithm is an effective tool for training models that are fair when demographic shift occurs. « less
Authors:
; ; ; ; ;
Award ID(s):
1763423
Publication Date:
NSF-PAR ID:
10334581
Journal Name:
Proceedings of the 10th International Conference on Learning Representations (ICLR)
Page Range or eLocation-ID:
1-24
Sponsoring Org:
National Science Foundation
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