论文标题
人工智能公平的固有局限性
Inherent Limitations of AI Fairness
论文作者
论文摘要
由于人工智能（AI）系统的现实影响一直在稳步增长，因此这些系统也受到越来越多的审查。作为回应，对AI公平性的研究已迅速发展为与计算机科学，社会科学，法律和哲学联系的丰富研究领域。已经提出了许多用于衡量和实现AI公平性的技术解决方案，但近年来，他们的方法因误导，不现实和有害而受到批评。 在我们的论文中，我们调查了对人工智能公平性的这些批评，并确定了AI公平典型范式所固有的关键局限性。通过仔细概述技术解决方案可以在多大程度上实现AI公平的程度，我们旨在提供必要的背景，以对公平AI中的发展产生细微的看法。该描述还为非AI解决方案提供了研究机会，以支持AI系统支持公平决策过程。
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.
