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.

翻译：人工智能模型现已广泛应用于医疗、教育、就业等生活的各个方面。由于它们被部署在许多敏感环境中，并做出可能改变人生的决定，潜在的偏见结果是一个紧迫的问题。开发者应确保此类模型不会表现出任何意外的歧视性行为，例如对特定性别、种族或残障人士的偏袒。随着人工智能系统的普遍传播，研究人员和从业者日益认识到不公平模型的存在，并致力于减轻其中的偏见。为解决此类问题，确保模型不会有意或无意地延续偏见，学界已开展了大量研究。本综述概述了研究人员在促进人工智能系统公平性方面的不同方式。我们探讨了现有文献中存在的不同公平性定义。通过分类不同类型的偏见，我们构建了一个全面的分类体系，并调查了不同应用领域中存在偏见的人工智能案例。我们对研究人员用于减轻人工智能模型偏见的各种方法和技术进行了深入研究。此外，我们还探讨了偏见模型对用户体验的影响，以及在开发和部署此类模型时应考虑的伦理问题。我们希望本综述能帮助研究人员和从业者理解人工智能系统中公平性与偏见的复杂细节。通过分享这份详尽的综述，我们旨在推动公平与负责任人工智能领域的进一步讨论。