  With the widespread use of AI systems and applications in our everyday lives,
it is important to take fairness issues into consideration while designing and
engineering these types of systems. Such systems can be used in many sensitive
environments to make important and life-changing decisions; thus, it is crucial
to ensure that the decisions do not reflect discriminatory behavior toward
certain groups or populations. We have recently seen work in machine learning,
natural language processing, and deep learning that addresses such challenges
in different subdomains. With the commercialization of these systems,
researchers are becoming aware of the biases that these applications can
contain and have attempted to address them. In this survey we investigated
different real-world applications that have shown biases in various ways, and
we listed different sources of biases that can affect AI applications. We then
created a taxonomy for fairness definitions that machine learning researchers
have defined in order to avoid the existing bias in AI systems. In addition to
that, we examined different domains and subdomains in AI showing what
researchers have observed with regard to unfair outcomes in the
state-of-the-art methods and how they have tried to address them. There are
still many future directions and solutions that can be taken to mitigate the
problem of bias in AI systems. We are hoping that this survey will motivate
researchers to tackle these issues in the near future by observing existing
work in their respective fields.
