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 …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

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 …

Abstract Machine learning (ML) is increasingly being used in high-stakes applications
impacting society. Therefore, it is of critical importance that ML models do not propagate …

Data scientists often develop data sets for analysis by drawing upon sources of data
available to them. A major challenge is to ensure that the data set used for analysis has an …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

We are being constantly judged by automated decision systems that have been widely
criticised for being discriminatory and unfair. Since an algorithm is only as good as the data …

Content spread inequity is a potential unfairness issue in online social networks, disparately
impacting minority groups. In this paper, we view friendship suggestion, a common feature in …

Classification, a heavily-studied data-driven machine learning task, drives an increasing
number of prediction systems involving critical human decisions such as loan approval and …

The potential harms and drawbacks of automated decision making has become a challenge
as data science blends into our lives. In particular, fairness issues with deployed machine …