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

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

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 …

Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML
systems that are already used in production. The main appeal of post-processing is that it …

Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the
drawbacks of group fairness. Despite its benefits, it depends on a task specific fair metric that …

Despite recent advances in achieving fair representations and predictions through
regularization, adversarial debiasing, and contrastive learning in graph neural networks …

Spurious features interfere with the goal of obtaining robust models that perform well across
many groups within the population. A natural remedy is to remove such features from the …

Deep learning algorithms mine knowledge from the training data and thus would likely
inherit the dataset's bias information. As a result, the obtained model would generalize …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Many instances of algorithmic bias are caused by distributional shifts. For example, machine
learning (ML) models often perform worse on demographic groups that are …