As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In response, the study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful. In our paper, we survey these criticisms of AI fairness and identify key limitations that are inherent to the prototypical paradigm of AI fairness. By carefully outlining the extent to which technical solutions can realistically help in achieving AI fairness, we aim to provide the background necessary to form a nuanced opinion on developments in fair AI. This delineation also provides research opportunities for non-AI solutions peripheral to AI systems in supporting fair decision processes.

翻译：随着人工智能系统对现实世界的影响持续增长，这些系统也受到了越来越多的审视。作为回应，人工智能公平性研究已迅速发展为一个与计算机科学、社会科学、法学和哲学相关联的丰富研究领域。尽管已有许多用于衡量和实现人工智能公平性的技术解决方案被提出，但近年来其方法因具有误导性、不切实际和有害性而受到批评。在本文中，我们梳理了这些对人工智能公平性的批评，并识别出人工智能公平性原型范式所固有的关键局限性。通过仔细阐明技术解决方案在现实中有助于实现人工智能公平性的程度，我们旨在为对公平人工智能的发展形成细致入微的观点提供必要背景。这种界定也为人工智能系统外围的非人工智能解决方案（以支持公平决策过程）提供了研究机会。