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 …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

Disparate impact has raised serious concerns in machine learning applications and its
societal impacts. In response to the need of mitigating discrimination, fairness has been …

Advances in language modeling architectures and the availability of large text corpora have
driven progress in automatic text generation. While this results in models capable of …

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