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 …

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

As the scope of machine learning broadens, we observe a recurring theme of algorithmic
monoculture: the same systems, or systems that share components (eg datasets, models) …

Evaluating new techniques on realistic datasets plays a crucial role in the development of
ML research and its broader adoption by practitioners. In recent years, there has been a …

Nowadays, Machine Learning (ML) systems are widely used in various businesses and are
increasingly being adopted to make decisions that can significantly impact people's lives …

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …

Recent work in algorithmic fairness has highlighted the challenge of defining racial
categories for the purposes of anti-discrimination. These challenges are not new but have …

Variance in predictions across different trained models is a significant, under-explored
source of error in fair binary classification. In practice, the variance on some data examples …

One of the core goals of responsible AI development is ensuring high-quality training
datasets. Many researchers have pointed to the importance of the annotation step in the …