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）正在几乎每个领域产生深远影响。其巨大成功的一个关键推动因素是丰富且高质量数据的可用性，用于构建机器学习模型。近年来，数据在人工智能中的作用显著增强，催生了以数据为中心的人工智能这一新兴概念。研究人员和从业者的关注点已逐渐从改进模型设计转向提升数据的质量与数量。在本综述中，我们首先讨论了以数据为中心的人工智能的必要性，随后从整体视角审视了三个通用数据目标（训练数据开发、推理数据开发和数据维护）及其代表性方法。我们还从自动化和协作的角度组织了现有文献，讨论了相关挑战，并整理了各类任务的基准数据集。我们相信这是首份全面综述，提供了数据生命周期各阶段各类任务的全局视野。希望它能帮助读者高效把握该领域的全貌，并为他们提供系统性工程化数据以构建人工智能系统的技术及进一步研究思路。一份以数据为中心的人工智能资源配套列表将定期在 https://github.com/daochenzha/data-centric-AI 更新。