A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

翻译：2016年，一套基础性的可查找、可访问、可互操作与可复用（FAIR）原则被提出，作为规范数据管理与管理的先决条件，旨在实现学术数据的可重用性。这些原则在宏观层面同样适用于其他数字资产。随着时间的推移，FAIR指导原则被重新阐释或扩展，以涵盖生成数据的软件、工具、算法及工作流程。当前，FAIR原则正被应用于人工智能模型与数据集领域。本文呈现了来自不同国家、学科背景的研究人员的视角、愿景与经验，他们正各自在实践社区中引领FAIR原则的定义与采纳进程。文章还探讨了推动并激励FAIR人工智能研究所可能产生的成果。本报告内容基于2022年6月7日在阿贡国家实验室举办的"面向人工智能的FAIR原则研讨会"。