Data-centric Artificial Intelligence: A Survey

TL;DR

This survey comprehensively reviews the emerging field of data-centric AI, emphasizing the importance of data quality and quantity across the AI development lifecycle, and discusses methods, challenges, and benchmarks.

Contribution

It is the first extensive survey providing a global view of data-centric AI tasks, methods, and challenges across various stages of data management in AI systems.

Findings

Highlights the shift from model-centric to data-centric AI approaches.

Organizes literature on automation and collaboration in data management.

Provides benchmarks and discusses challenges in data-centric AI.

Abstract

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