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.

翻译：人工智能在医疗决策、医学诊断及其他领域的重大进展，同时引发了对其系统公平性与偏见的关切。这在医疗、就业、刑事司法、信用评分等领域尤为关键，且日益涉及生成合成媒体的生成式人工智能模型（GenAI）。此类系统可能导致不公平结果并加剧现有不平等，包括影响个体在合成数据中表征的生成偏见。本综述论文对人工智能中的公平性与偏见提供简洁全面的概述，探讨其来源、影响及缓解策略。我们回顾了数据偏见、算法偏见及人类决策偏见等来源，特别强调了新兴的生成式人工智能偏见问题——即模型可能再现并放大社会刻板印象。我们评估了具有偏见的人工智能系统的社会影响，重点关注其对不平等的固化及有害刻板印象的强化，尤其在生成式人工智能日益普及、创造影响公众认知的内容背景下。我们探讨了多种提出的缓解策略，讨论了其实施中的伦理考量，并强调需跨学科协作以确保有效性。通过系统性的多学科文献综述，我们定义了人工智能偏见及其不同类型，包括对生成式人工智能偏见的详细审视。我们论述了人工智能偏见对个体和社会的负面影响，并概述当前缓解人工智能偏见的方法，包括数据预处理、模型选择和后处理。我们强调了生成式人工智能模型带来的独特挑战以及针对这些挑战定制策略的重要性。