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：人工智能正在几乎所有领域产生深远影响。其巨大成功的关键推动因素之一是能够获得丰富且高质量的数据来构建机器学习模型。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注意力逐渐从推进模型设计转向提升数据的质量与数量。在本综述中，我们首先探讨以数据为中心的人工智能的必要性，随后从整体视角审视三个一般性数据目标（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角度对现有文献进行梳理，讨论面临的挑战，并整理各类任务的基准测试。我们相信，这是首份全面综述，提供了跨数据生命周期各阶段多种任务的全局视角。希望它能帮助读者高效掌握该领域的全貌，并赋予他们系统化工程化数据以构建人工智能系统的技术及后续研究思路。一份以数据为中心的人工智能资源配套列表将定期更新于 https://github.com/daochenzha/data-centric-AI。