@inproceedings{615690270891421ea98fcb3925fd9669,
title = "DCAI: Data-centric Artificial Intelligence",
abstract = "The emergence of Data-centric AI (DCAI) represents a pivotal shift in AI development, redirecting focus from model refinement to prioritizing data quality. This paradigmatic transition emphasizes the critical role of data in AI. While past approaches centered on refining models, they often overlooked potential data imperfections, raising questions about the true potential of enhanced model performance. DCAI advocates the systematic engineering of data, complementing existing efforts and playing a vital role in driving AI success. This transition has spurred innovation in various machine learning and data mining algorithms and their applications on the Web. Therefore, we propose the DCAI Workshop at WWW{\textquoteright}24, which offers a platform for academic researchers and industry practitioners to showcase the latest advancements in DCAI research and their practical applications in the real world.",
keywords = "Data Augmentation, Data Evaluation, Data Optimization, Data Reduction, Data Selection, Data-centric AI",
author = "Wei Jin and Haohan Wang and Daochen Zha and Qiaoyu Tan and Yao Ma and Sharon Li and Lee, {Su In}",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 33rd ACM Web Conference, WWW 2024 ; Conference date: 13-05-2024 Through 17-05-2024",
year = "2024",
month = may,
day = "13",
doi = "10.1145/3589335.3641297",
language = "English (US)",
series = "WWW 2024 Companion - Companion Proceedings of the ACM Web Conference",
publisher = "Association for Computing Machinery",
pages = "1482--1485",
booktitle = "WWW 2024 Companion - Companion Proceedings of the ACM Web Conference",
address = "United States",
}