Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI

翻译：人工智能（AI）正深刻影响着几乎所有领域。其巨大成功的关键驱动力之一是能够获取丰富且高质量的数据来构建机器学习模型。近年来，数据在AI中的作用被显著放大，催生了以数据为中心的人工智能这一新兴概念。研究人员和实践者的关注点已逐渐从模型设计优化转向提升数据的质量与数量。本综述首先探讨以数据为中心的人工智能的必要性，随后从整体视角介绍三项通用数据目标（训练数据开发、推理数据开发及数据维护）及其代表性方法。我们还从自动化与协作的角度梳理现有文献，讨论相关挑战，并整理各类任务的基准测试。据我们所知，这是首份全面概述数据生命周期各阶段多样化任务的综述。我们期望它能帮助读者高效把握这一领域的全貌，并为其系统化工程化数据以构建AI系统提供技术与进一步的研究思路。配套的数据中心AI资源列表将定期更新于 https://github.com/daochenzha/data-centric-AI。