This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias
often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing …

Real-world applications of machine learning tools in high-stakes domains are often
regulated to be fair, in the sense that the predicted target should satisfy some quantitative …

Disparate impact has raised serious concerns in machine learning applications and its
societal impacts. In response to the need of mitigating discrimination, fairness has been …

With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …

We propose an analysis in fair learning that preserves the utility of the data while reducing
prediction disparities under the criteria of group sufficiency. We focus on the scenario where …

Current large-scale language models can be politically biased as a result of the data they
are trained on, potentially causing serious problems when they are deployed in real-world …

A critical concern in data-driven decision making is to build models whose outcomes do not
discriminate against some demographic groups, including gender, ethnicity, or age. To …

To mitigate the bias exhibited by machine learning models, fairness criteria can be
integrated into the training process to ensure fair treatment across all demographics, but it …