Supervised Algorithmic Fairness in Distribution Shifts: A Survey
arxiv(2024)
摘要
Supervised fairness-aware machine learning under distribution shifts is an
emerging field that addresses the challenge of maintaining equitable and
unbiased predictions when faced with changes in data distributions from source
to target domains. In real-world applications, machine learning models are
often trained on a specific dataset but deployed in environments where the data
distribution may shift over time due to various factors. This shift can lead to
unfair predictions, disproportionately affecting certain groups characterized
by sensitive attributes, such as race and gender. In this survey, we provide a
summary of various types of distribution shifts and comprehensively investigate
existing methods based on these shifts, highlighting six commonly used
approaches in the literature. Additionally, this survey lists publicly
available datasets and evaluation metrics for empirical studies. We further
explore the interconnection with related research fields, discuss the
significant challenges, and identify potential directions for future studies.
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