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 …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

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

Conventional algorithmic fairness is West-centric, as seen in its subgroups, values, and
methods. In this paper, we de-center algorithmic fairness and analyse AI power in India …

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers
trained on such datasets can inherit these biases. In this paper, we provide a mathematical …

The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data.
Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air …

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

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

Internet search affects people's cognition of the world, so mitigating biases in search results
and learning fair models is imperative for social good. We study a unique gender bias in …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …