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 …

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 …

Federated learning involves training statistical models in massive, heterogeneous networks.
Naively minimizing an aggregate loss function in such a network may disproportionately …

Fairness in visual recognition is becoming a prominent and critical topic of discussion as
recognition systems are deployed at scale in the real world. Models trained from data in …

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the
existing mainstream approaches mostly require training or finetuning the entire weights of …

Federated learning is an emerging framework that builds centralized machine learning
models with training data distributed across multiple devices. Most of the previous works …

Empirical risk minimization (ERM) is typically designed to perform well on the average loss,
which can result in estimators that are sensitive to outliers, generalize poorly, or treat …

We consider the problem of producing fair probabilistic classifiers for multi-class
classification tasks. We formulate this problem in terms of``projecting''a pre-trained (and …

The min-max optimization problem, also known as the<; i> saddle point problem<;/i>, is a
classical optimization problem that is also studied in the context of zero-sum games. Given a …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …