Data Readiness for AI: A 360-Degree Survey
Kaveen Hiniduma Suren Byna Jean Luca Bez
Data are the critical fuel for Artificial Intelligence (AI) models. Poor
quality data produces inaccurate and ineffective AI models that may lead to
incorrect or unsafe use. Checking for data readiness is a crucial step in
improving data quality. Numerous 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 for verifying AI's data readiness. This survey examines
more than 120 papers that are published by ACM Digital Library, IEEE Xplore,
other reputable journals, and articles published on the web 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 can lead to new standards for DRAI metrics that would be used for
enhancing the quality and accuracy of AI training and inference.