This survey by Ninareh Mehrabi, Fred Morstatter, Nrripsuta Saxena, Kristina Lerman, and Aram Galstyan from USC-isi explores the importance of fairness in machine learning systems, particularly in the context of their widespread use in everyday life. The authors highlight the need to ensure that AI systems do not reflect discriminatory behavior towards certain groups or populations. They review real-world applications that have shown biases and identify different sources of biases affecting AI applications. The survey also presents a taxonomy of fairness definitions to help researchers avoid existing biases in AI systems. Additionally, it examines various domains and subdomains in AI, discussing observed unfair outcomes and proposed solutions. The authors emphasize the importance of addressing biases in data, algorithms, and user experiences to mitigate the problem of bias in AI systems. They conclude by outlining future directions and potential solutions to tackle these issues.This survey by Ninareh Mehrabi, Fred Morstatter, Nrripsuta Saxena, Kristina Lerman, and Aram Galstyan from USC-isi explores the importance of fairness in machine learning systems, particularly in the context of their widespread use in everyday life. The authors highlight the need to ensure that AI systems do not reflect discriminatory behavior towards certain groups or populations. They review real-world applications that have shown biases and identify different sources of biases affecting AI applications. The survey also presents a taxonomy of fairness definitions to help researchers avoid existing biases in AI systems. Additionally, it examines various domains and subdomains in AI, discussing observed unfair outcomes and proposed solutions. The authors emphasize the importance of addressing biases in data, algorithms, and user experiences to mitigate the problem of bias in AI systems. They conclude by outlining future directions and potential solutions to tackle these issues.