An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

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 …

As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Fairness is an increasingly important concern as machine learning models are used to
support decision making in high-stakes applications such as mortgage lending, hiring, and …

Tabular data underpins numerous high-impact applications of machine learning from fraud
detection to genomics and healthcare. Classical approaches to solving tabular problems …

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data
collaboration without revealing their private data to each other. Recently, vertical FL, where …

Understanding the inner workings of complex machine learning models is a long-standing
problem and most recent research has focused on local interpretability. To assess the role of …

Dimensionality reduction methods, also known as projections, are frequently used in
multidimensional data exploration in machine learning, data science, and information …