Supervised Algorithmic Fairness in Distribution Shifts: A Survey

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. There are two main lines of distribution shifts: general and fairness-specific distribution shifts. The former focuses on shifts involving the input features and labels. Covariate shift refers to variations due to differences between the set of marginal distributions over instances. Label shift indicates changes in the distribution of the class variable. Concept shift refers to functional relation change due to the change amongst the instance-conditional distributions. Fairness-specific approaches consider sensitive attributes. Demographic shift refers to certain sensitive population subgroups becoming more or less probable during inference. Dependence shift captures the correlation change between labels and sensitive attributes. Within these distribution shifts, the fairness of the trained model is directly impacted and may deteriorate when adapted to target domains. To enhance the performance of fairness generalization under distribution shifts, we thoroughly examine existing supervised fairness-aware machine learning methods and highlight six commonly used approaches in the literature. Methods include feature disentanglement and data augmentation to capture invariance; causal graphs and paths to identify and address unfair factors; reweighting as a pre-processing method; robust optimization to minimize worst-case loss; and regularization-based methods to reduce correlation between representations and sensitive attributes and enable fairness transfer across domains. Our main contributions of this survey are summarized: * We summarize a list of different types of distribution shifts and illustrate the effectiveness of generalizing a fairness-aware classifier from source to target domains in the context of each distribution shift. * We categorize existing methods based on different distribution shifts and highlight six main approaches commonly used for handling such shifts. * We compile a list of publicly available datasets and survey the literature to identify commonly used evaluation metrics for quantifying fairness. * We point out significant challenges and explore several future directions for studying fairness under distribution shifts.