Data Readiness for AI: A 360-Degree Survey
This work addresses the need for standardized data readiness metrics to enhance AI reliability and safety, but it is incremental as it builds on existing R&D efforts through a survey-based approach.
The paper tackles the problem of evaluating data readiness for AI training by conducting a comprehensive survey of over 140 sources to propose a taxonomy of metrics, aiming to establish new standards for improving AI model quality, accuracy, and fairness.
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.