# Data-centric AI: Perspectives and Challenges

arXiv: https://arxiv.org/abs/2301.04819 · HTML: https://mlsanity.com/paper/2301.04819/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [TODS: An Automated Time Series Outlier Detection System](https://mlsanity.com/paper/2009.09822/) (2020)
- [Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning](https://mlsanity.com/paper/2009.07415/) (2020)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021)
- [DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering](https://mlsanity.com/paper/1911.00061/) (2019)
- [Learning Credible Deep Neural Networks with Rationale Regularization](https://mlsanity.com/paper/1908.05601/) (2019)
- [DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples](https://mlsanity.com/paper/2110.13740/) (2021)
- [Multi-label Dataless Text Classification with Topic Modeling](https://mlsanity.com/paper/1711.01563/) (2017)
- [Towards Similarity-Aware Time-Series Classification](https://mlsanity.com/paper/2201.01413/) (2022)
- [Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2208.12433/) (2022)
- [AutoVideo: An Automated Video Action Recognition System](https://mlsanity.com/paper/2108.04212/) (2021)

## Built on by (most cited first, 30 of 43)

- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023)
- [A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation](https://mlsanity.com/paper/2402.03358/) (2024)
- [Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning](https://mlsanity.com/paper/2312.17493/) (2023)
- [FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions](https://mlsanity.com/paper/2305.17718/) (2023)
- [OpenGSL: A Comprehensive Benchmark for Graph Structure Learning](https://mlsanity.com/paper/2306.10280/) (2023)
- [ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs](https://mlsanity.com/paper/2305.03513/) (2023)
- [MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale](https://mlsanity.com/paper/2604.04771/) (2026)
- [Generative Dataset Distillation: Balancing Global Structure and Local Details](https://mlsanity.com/paper/2404.17732/) (2024)
- [Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders](https://mlsanity.com/paper/2405.01460/) (2024)
- [From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data](https://mlsanity.com/paper/2406.19292/) (2024)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [ActiveGLAE: A Benchmark for Deep Active Learning with Transformers](https://mlsanity.com/paper/2306.10087/) (2023)
- [Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks](https://mlsanity.com/paper/2311.01038/) (2023)
- [Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising](https://mlsanity.com/paper/2310.09126/) (2023)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer](https://mlsanity.com/paper/2304.07874/) (2023)
- [VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models](https://mlsanity.com/paper/2309.16211/) (2023)
- [Context-aware Domain Adaptation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07453/) (2023)
- [XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach](https://mlsanity.com/paper/2407.11771/) (2024)
- [Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective](https://mlsanity.com/paper/2412.09712/) (2024)
- [Promoting Fairness in Link Prediction with Graph Enhancement](https://mlsanity.com/paper/2409.08658/) (2024)
- [GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning](https://mlsanity.com/paper/2505.02027/) (2025)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [Transition Role of Entangled Data in Quantum Machine Learning](https://mlsanity.com/paper/2306.03481/) (2023)
- [WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking](https://mlsanity.com/paper/2411.09820/) (2024)
- [Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge](https://mlsanity.com/paper/2505.02784/) (2025)
- [Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applications](https://mlsanity.com/paper/2408.06724/) (2024)
- [Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research](https://mlsanity.com/paper/2406.04116/) (2024)

## Abstract (arXiv)

The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability. Although our community has continuously invested efforts into enhancing data in different aspects, they are often isolated initiatives on specific tasks. To facilitate the collective initiative in our community and push forward DCAI, we draw a big picture and bring together three general missions: training data development, inference data development, and data maintenance. We provide a top-level discussion on representative DCAI tasks and share perspectives. Finally, we list open challenges. More resources are summarized at https://github.com/daochenzha/data-centric-AI