Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Robustness has been studied in many domains of AI, yet with different interpretations across domains and contexts. In this work, we systematically survey the recent progress to provide a reconciled terminology of concepts around AI robustness. We introduce three taxonomies to organize and describe the literature both from a fundamental and applied point of view: 1) robustness by methods and approaches in different phases of the machine learning pipeline; 2) robustness for specific model architectures, tasks, and systems; and in addition, 3) robustness assessment methodologies and insights, particularly the trade-offs with other trustworthiness properties. Finally, we identify and discuss research gaps and opportunities and give an outlook on the field. We highlight the central role of humans in evaluating and enhancing AI robustness, considering the necessary knowledge humans can provide, and discuss the need for better understanding practices and developing supportive tools in the future.

翻译：尽管人工智能系统取得了令人印象深刻的业绩,但其稳健性仍然难以实现,并构成妨碍大规模采纳的关键问题。对大赦国际许多领域的强健性进行了研究,但在不同领域和背景中作了不同的解释。在这项工作中,我们系统地调查了最近的进展,以提供与大赦国际稳健性概念相协调的术语。我们从基本和实用的角度引入了三个分类,以组织和描述文献:1) 机器学习管道不同阶段的方法和方法的稳健性;2) 具体模型结构、任务和系统的稳健性;以及3) 稳健性评估方法和洞察力,特别是与其他可信赖性属性的权衡。最后,我们确定和讨论研究差距和机会,并介绍该领域的前景。我们强调人类在评价和增强AI稳健性方面的核心作用,考虑到人类能够提供的必要知识,并讨论今后需要更好地了解各种做法和开发支助性工具。