  As Machine Learning technologies become increasingly used in contexts that
affect citizens, companies as well as researchers need to be confident that
their application of these methods will not have unexpected social
implications, such as bias towards gender, ethnicity, and/or people with
disabilities. There is significant literature on approaches to mitigate bias
and promote fairness, yet the area is complex and hard to penetrate for
newcomers to the domain. This article seeks to provide an overview of the
different schools of thought and approaches to mitigating (social) biases and
increase fairness in the Machine Learning literature. It organises approaches
into the widely accepted framework of pre-processing, in-processing, and
post-processing methods, subcategorizing into a further 11 method areas.
Although much of the literature emphasizes binary classification, a discussion
of fairness in regression, recommender systems, unsupervised learning, and
natural language processing is also provided along with a selection of
currently available open source libraries. The article concludes by summarising
open challenges articulated as four dilemmas for fairness research.
