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, particularly in areas like healthcare, employment, criminal justice, and credit scoring. Such systems can lead to unfair outcomes and perpetuate existing inequalities. 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, and assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes. 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, and discuss the negative impacts of AI bias on individuals and society. We also provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. Addressing bias in AI requires a holistic approach, involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias.

翻译：人工智能在医疗决策、医学诊断等领域取得显著进展的同时，也引发了对AI系统公平性与偏见的担忧，尤其是在医疗、就业、刑事司法和信用评分等领域。此类系统可能导致不公平结果并加剧现有不平等现象。本综述论文对AI中的公平性与偏见进行了简洁而全面的概述，探讨其来源、影响及缓解策略。我们回顾了偏见来源（如数据偏见、算法偏见和人类决策偏见），评估了偏见的AI系统对社会的影响，重点关注其对不平等现象的加剧以及对有害刻板印象的强化。我们探讨了多种拟议的缓解策略，讨论了实施过程中的伦理考量，并强调跨学科合作对确保有效性的必要性。通过跨多个学科的系统文献综述，我们给出了AI偏见的定义及其不同类型，并讨论了AI偏见对个人和社会的负面影响。我们还概述了当前缓解AI偏见的方法，包括数据预处理、模型选择和后处理。解决AI偏见需要整体性方法，包括采用多样化和具有代表性的数据集、增强AI系统的透明度和问责制，以及探索优先考虑公平性和伦理考量的替代AI范式。本综述通过概述AI偏见的来源、影响及缓解策略，为构建公平、无偏见的AI系统这一持续议题做出了贡献。