# Data Readiness for AI: A 360-Degree Survey

arXiv: https://arxiv.org/abs/2404.05779 · HTML: https://mlsanity.com/paper/2404.05779/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 164 references that are in the map.
- First posted on arXiv 8 Apr 2024.

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

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Systematic Evaluation of Privacy Risks of Machine Learning Models](https://mlsanity.com/paper/2003.10595/) (2020)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [The Privacy Onion Effect: Memorization is Relative](https://mlsanity.com/paper/2206.10469/) (2022)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [An ADMM Based Framework for AutoML Pipeline Configuration](https://mlsanity.com/paper/1905.00424/) (2019)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Anomaly Detection by Robust Statistics](https://mlsanity.com/paper/1707.09752/) (2017)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [Optimal Ratio for Data Splitting](https://mlsanity.com/paper/2202.03326/) (2022)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Data Readiness Levels](https://mlsanity.com/paper/1705.02245/) (2017)
- [Technical Privacy Metrics: a Systematic Survey](https://mlsanity.com/paper/1512.00327/) (2015)
- [Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem](https://mlsanity.com/paper/1901.10173/) (2019)
- [SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning](https://mlsanity.com/paper/2112.02230/) (2021)
- [FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy](https://mlsanity.com/paper/2207.00611/) (2022)

## Built on by (most cited first, 15 of 15)

- [SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models](https://mlsanity.com/paper/2503.13503/) (2025)
- [AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI](https://mlsanity.com/paper/2406.19256/) (2024)
- [I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey](https://mlsanity.com/paper/2404.10386/) (2024)
- [AIDRIN 2.0: A Framework to Assess Data Readiness for AI](https://mlsanity.com/paper/2505.18213/) (2025)
- [Data Readiness for Scientific AI at Scale](https://mlsanity.com/paper/2507.23018/) (2025)
- [Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data](https://mlsanity.com/paper/2506.24039/) (2025)
- [Automated Data Readiness for Scientific AI](https://mlsanity.com/paper/2607.02771/) (2026)
- [CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2505.23849/) (2025)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework](https://mlsanity.com/paper/2509.01565/) (2025)
- [SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data](https://mlsanity.com/paper/2604.26645/) (2026)
- [DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data](https://mlsanity.com/paper/2605.17866/) (2026)
- [Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration](https://mlsanity.com/paper/2606.28856/) (2026)
- [SetGo: Metadata Readiness for Scientific AI Datasets](https://mlsanity.com/paper/2607.22677/) (2026)
- [Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale](https://mlsanity.com/paper/2608.19625/) (2026)

## 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.