论文标题
AI公平：跨学科和国际社会建筑观点
FAIR for AI: An interdisciplinary and international community building perspective
论文作者
论文摘要
2016年提出了一组可发现的，可访问，可互操作和可重复使用的（公平）原则，作为适当数据管理和管理的先决条件，目的是使学术数据的可重复使用。这些原则还旨在在高级的其他数字资产中应用，随着时间的流逝，公平的指导原则已被重新解释或扩展，以包括软件，工具，算法和产生数据的工作流程。现在，公平的原则正在适应AI模型和数据集的背景下。在这里，我们介绍了来自不同国家，学科和背景的研究人员的观点，愿景和经验，这些研究人员在其实践社区中领导和采用公平原则的定义和采用，并讨论通过追求和激励公平的AI研究而产生的结果。该报告的材料基于2022年6月7日在Argonne国家实验室举行的AI研讨会。
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.
