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 …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

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 …

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions
that have far‐reaching impact on individuals and society. Their decisions might affect …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Most of existing work on fairness assumes available demographic information in the training
set. In practice, due to legal or privacy concerns, when demographic information is not …

The recent advance of the federated learning (FL) has brought new opportunities for privacy-
aware distributed machine learning (ML) applications to train a powerful ML model without …

AI-driven decision-making can lead to discrimination against certain individuals or social
groups based on protected characteristics/attributes such as race, gender, or age. The …

Automated decision making based on big data and machine learning (ML) algorithms can
result in discriminatory decisions against certain protected groups defined upon personal …