mlsanityData to 7 Oct 2026

Data Readiness for AI: A 360-Degree Survey

arXiv 2404.05779 ↗First posted 8 Apr 2024cs.LG cs.AIcited by 15

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Abstract (arXiv)

Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that will be used for enhancing the quality, accuracy, and fairness of AI training and inference.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 25 of its 164 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

25 of 164 references.

References · 25

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2018Datasheets for Datasetscited by 1,371
  4. 2018Model Cards for Model Reportingcited by 1,068
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  6. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  7. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  8. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 273
  9. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 193
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  11. 2022Data Banzhaf: A Robust Data Valuation Framework for Machine Learningcited by 126
  12. 2018The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standardscited by 97
13 more
  1. 2017Practical Coreset Constructions for Machine Learningcited by 75
  2. 2016Feature Selection: A Data Perspectivecited by 50
  3. 2019An ADMM Based Framework for AutoML Pipeline Configurationcited by 47
  4. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  5. 2017Anomaly Detection by Robust Statisticscited by 22
  6. 2021Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasetscited by 18
  7. 2020Data Readiness Reportcited by 17
  8. 2022Optimal Ratio for Data Splittingcited by 17
  9. 2017Data Readiness Levelscited by 12
  10. 2015Technical Privacy Metrics: a Systematic Surveycited by 11
  11. 2019Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problemcited by 10
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  13. 2022FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopycited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 6.

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  1. 2025SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Modelscited by 15
  2. 2024AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AIcited by 9
  3. 2024I/O in Machine Learning Applications on HPC Systems: A 360-degree Surveycited by 3
  4. 2025AIDRIN 2.0: A Framework to Assess Data Readiness for AIcited by 2
  5. 2025Data Readiness for Scientific AI at Scalecited by 2
  6. 2025Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Datacited by 1
  7. 2026Automated Data Readiness for Scientific AIcited by 1
  8. 2025CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learningcited by 0
  9. 2025AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directionscited by 0
  10. 2025Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Frameworkcited by 0
  11. 2026SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Datacited by 0
  12. 2026DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Datacited by 0
3 more
  1. 2026Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Explorationcited by 0
  2. 2026SetGo: Metadata Readiness for Scientific AI Datasetscited by 0
  3. 2026Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scalecited by 0