Data is a crucial infrastructure to how artificial intelligence (AI) systems learn. However, these systems to date have been largely model-centric, putting a premium on the model at the expense of the data quality. Data quality issues beset the performance of AI systems, particularly in downstream deployments and in real-world applications. Data-centric AI (DCAI) as an emerging concept brings data, its quality and its dynamism to the forefront in considerations of AI systems through an iterative and systematic approach. As one of the first overviews, this article brings together data-centric perspectives and concepts to outline the foundations of DCAI. It specifically formulates six guiding principles for researchers and practitioners and gives direction for future advancement of DCAI.

翻译：数据是人工智能系统学习的关键基础设施。然而，迄今为止，这些系统在很大程度上以模型为中心，优先考虑模型而忽视了数据质量。数据质量问题困扰着AI系统的性能，尤其是在下游部署和实际应用中。以数据为中心的AI（DCAI）作为一个新兴概念，通过迭代和系统化的方法，将数据、其质量及动态性置于AI系统考量的核心。作为首批综述之一，本文汇集了以数据为中心的视角与概念，概述了DCAI的基础。文章特别为研究人员和实践者制定了六项指导原则，并为DCAI的未来发展指明了方向。