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 …

The existence of spurious correlations such as image backgrounds in the training
environment can make empirical risk minimization (ERM) perform badly in the test …

As machine learning has been deployed ubiquitously across applications in modern data
science, algorithmic fairness has become a great concern. Among them, imposing fairness …

Recent studies suggest that computer vision models come at the risk of compromising
fairness. There are extensive works to alleviate unfairness in computer vision using pre …

AI methods are used in societally important settings, ranging from credit to employment to
housing, and it is crucial to provide fairness in regard to algorithmic decision making …

Large Language Models (LLMs), such as GPT-4, StarCoder, and Code Llama, are
transforming the way developers approach programming by automatically generating code …

Society is increasingly relying on predictive models in fields like criminal justice, credit risk
management, and hiring. To prevent such automated systems from discriminating against …

Bias issues of neural networks garner significant attention along with its promising
advancement. Among various bias issues, mitigating two predominant biases is crucial in …

Existing work on fairness modeling commonly assumes that sensitive attributes for all
instances are fully available, which may not be true in many real-world applications due to …

While the accuracy-fairness trade-off has been frequently observed in the literature of fair
machine learning, rigorous theoretical analyses have been scarce. To demystify this long …