  The significant advancements in applying Artificial Intelligence (AI) to
healthcare decision-making, medical diagnosis, and other domains have
simultaneously raised concerns about the fairness and bias of AI systems. This
is particularly critical in areas like healthcare, employment, criminal
justice, credit scoring, and increasingly, in generative AI models (GenAI) that
produce synthetic media. Such systems can lead to unfair outcomes and
perpetuate existing inequalities, including generative biases that affect the
representation of individuals in synthetic data. This survey paper offers a
succinct, comprehensive overview of fairness and bias in AI, addressing their
sources, impacts, and mitigation strategies. We review sources of bias, such as
data, algorithm, and human decision biases - highlighting the emergent issue of
generative AI bias where models may reproduce and amplify societal stereotypes.
We assess the societal impact of biased AI systems, focusing on the
perpetuation of inequalities and the reinforcement of harmful stereotypes,
especially as generative AI becomes more prevalent in creating content that
influences public perception. We explore various proposed mitigation
strategies, discussing the ethical considerations of their implementation and
emphasizing the need for interdisciplinary collaboration to ensure
effectiveness. Through a systematic literature review spanning multiple
academic disciplines, we present definitions of AI bias and its different
types, including a detailed look at generative AI bias. We discuss the negative
impacts of AI bias on individuals and society and provide an overview of
current approaches to mitigate AI bias, including data pre-processing, model
selection, and post-processing. We emphasize the unique challenges presented by
generative AI models and the importance of strategies specifically tailored to
address these.
