Machine learning focuses on developing models for datasets, but real-world data is often messy. Improving the dataset itself can be a better way to enhance performance instead of just improving the models. Data-Centric AI (DCAI) is an emerging field that systematically improves datasets, resulting in significant improvements in ML applications. DCAI treats data improvement as an engineering discipline, offering a shift in focus from modeling to the underlying data. This workshop aims to build an interdisciplinary DCAI community to tackle data problems such as collection, labeling, preprocessing, quality evaluation, debt, and governance. Interested parties can shape the future of AI and ML by submitting papers in response to the call for papers.

We welcome a wide array of submissions focused on data-centric AI, encompassing topics such as theories, algorithms, applications, systems, and tools. These topics include but are not limited to:

• Automated Data Science Methods
  • Data cleaning, denoising, and interpolation
  • Feature selection and generation
  • Data refinement, feature-instance joint selection
  • Data quality improvement, representation learning, reconstruction
  • Outlier detection and removal

• Tools and Methodologies for Expediting Open-source Dataset Preparation
  • Time acceleration tools for sourcing and preparing high-quality data
  • Tools for consistent data labeling, data quality improvement
  • Tools for generating high-quality supervised learning training data
  • Tools for dataset control, high-level editing, searching public resources
  • Tools for dataset feedback incorporation, coverage understanding, editing
  • Dataset importers and exporters for easy data combination and consumption
  • System architectures and interfaces for dataset tool composition

• Algorithms for Handling Limited Labeled Data and Label Efficiency
  • Data selection techniques, semi-supervised learning, few-shot learning
  • Weak supervision methods, transfer learning, self-supervised learning approaches

• Algorithms for Dealing with Biased, Shifted, Drifted, and Out of Distribution Data
  • Datasets for bias evaluation and analysis
  • Algorithms for automated bias elimination, model training with biased data