  Artificial Intelligence (AI) models are now being utilized in all facets of
our lives such as healthcare, education and employment. Since they are used in
numerous sensitive environments and make decisions that can be life altering,
potential biased outcomes are a pressing matter. Developers should ensure that
such models don't manifest any unexpected discriminatory practices like
partiality for certain genders, ethnicities or disabled people. With the
ubiquitous dissemination of AI systems, researchers and practitioners are
becoming more aware of unfair models and are bound to mitigate bias in them.
Significant research has been conducted in addressing such issues to ensure
models don't intentionally or unintentionally perpetuate bias. This survey
offers a synopsis of the different ways researchers have promoted fairness in
AI systems. We explore the different definitions of fairness existing in the
current literature. We create a comprehensive taxonomy by categorizing
different types of bias and investigate cases of biased AI in different
application domains. A thorough study is conducted of the approaches and
techniques employed by researchers to mitigate bias in AI models. Moreover, we
also delve into the impact of biased models on user experience and the ethical
considerations to contemplate when developing and deploying such models. We
hope this survey helps researchers and practitioners understand the intricate
details of fairness and bias in AI systems. By sharing this thorough survey, we
aim to promote additional discourse in the domain of equitable and responsible
AI.
