# Data-Centric Artificial Intelligence arXiv: https://arxiv.org/abs/2212.11854 ยท HTML: https://mlsanity.com/paper/2212.11854/ ## Key facts - Cited by 31 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 69 references that are in the map. - First posted on arXiv 22 Dec 2022. ## Builds on (most cited first, 14 of 14) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020) - [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021) - [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022) - [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021) - [Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?](https://mlsanity.com/paper/1912.08320/) (2019) - [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021) - [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022) - [DEAL: Deep Evidential Active Learning for Image Classification](https://mlsanity.com/paper/2007.11344/) (2020) - [Artificial Intelligence Governance for Businesses](https://mlsanity.com/paper/2011.10672/) (2020) - [Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets](https://mlsanity.com/paper/2011.02738/) (2020) - [Machine Learning in Artificial Intelligence: Towards a Common Understanding](https://mlsanity.com/paper/2004.04686/) (2020) ## Built on by (most cited first, 30 of 31) - [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023) - [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023) - [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023) - [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023) - [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024) - [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://mlsanity.com/paper/2511.10192/) (2025) - [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024) - [Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development](https://mlsanity.com/paper/2407.11784/) (2024) - [Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective](https://mlsanity.com/paper/2412.09712/) (2024) - [Transition Role of Entangled Data in Quantum Machine Learning](https://mlsanity.com/paper/2306.03481/) (2023) - [General Information Metrics for Improving AI Model Training Efficiency](https://mlsanity.com/paper/2501.02004/) (2025) - [Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting](https://mlsanity.com/paper/2407.19784/) (2024) - [Med-CRAFT: An Information System for Explainable and Configurable Construction of Multimodal Medical QA Datasets](https://mlsanity.com/paper/2512.01045/) (2025) - [Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development](https://mlsanity.com/paper/2607.20532/) (2026) - [UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition](https://mlsanity.com/paper/2307.11170/) (2023) - [Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models](https://mlsanity.com/paper/2407.01608/) (2024) - [Analytics Modelling over Multiple Datasets using Vector Embeddings](https://mlsanity.com/paper/2502.17060/) (2025) - [Data Quality Challenges in Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.00552/) (2025) - [End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment](https://mlsanity.com/paper/2512.19723/) (2025) - [Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study](https://mlsanity.com/paper/2602.14357/) (2026) - [Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction](https://mlsanity.com/paper/2407.14210/) (2024) - [From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience](https://mlsanity.com/paper/2506.16051/) (2025) - [Out-of-distribution data supervision towards biomedical semantic segmentation](https://mlsanity.com/paper/2507.12105/) (2025) - [Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation](https://mlsanity.com/paper/2508.02281/) (2025) - [Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning](https://mlsanity.com/paper/2508.16255/) (2025) - [Optimizing Class Distributions for Bias-Aware Multi-Class Learning](https://mlsanity.com/paper/2509.11588/) (2025) - [A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models](https://mlsanity.com/paper/2509.24420/) (2025) - [AI Application Operations -- A Socio-Technical Framework for Data-driven Organizations](https://mlsanity.com/paper/2601.06061/) (2025) - [QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption](https://mlsanity.com/paper/2602.03686/) (2026) - [Crowdsourcing of Real-world Image Annotation via Visual Properties](https://mlsanity.com/paper/2604.14449/) (2026) ## Abstract (arXiv) Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems. The objective of this article is to introduce practitioners and researchers from the field of Information Systems (IS) to data-centric AI. We define relevant terms, provide key characteristics to contrast the data-centric paradigm to the model-centric one, and introduce a framework for data-centric AI. We distinguish data-centric AI from related concepts and discuss its longer-term implications for the IS community.