"Garbage In Garbage Out" is a universally agreed quote by computer scientists
from various domains, including Artificial Intelligence (AI). As data is the
fuel for AI, models trained on low-quality, biased data are often ineffective.
Computer scientists who use AI invest a considerable amount of time and effort
in preparing the data for AI. However, there are no standard methods or
frameworks for assessing the "readiness" of data for AI. To provide a
quantifiable assessment of the readiness of data for AI processes, we define
parameters of AI data readiness and introduce AIDRIN (AI Data Readiness
Inspector). AIDRIN is a framework covering a broad range of readiness
dimensions available in the literature that aid in evaluating the readiness of
data quantitatively and qualitatively. AIDRIN uses metrics in traditional data
quality assessment such as completeness, outliers, and duplicates for data
evaluation. Furthermore, AIDRIN uses metrics specific to assess data for AI,
such as feature importance, feature correlations, class imbalance, fairness,
privacy, and FAIR (Findability, Accessibility, Interoperability, and
Reusability) principle compliance. AIDRIN provides visualizations and reports
to assist data scientists in further investigating the readiness of data. The
AIDRIN framework enhances the efficiency of the machine learning pipeline to
make informed decisions on data readiness for AI applications.

评估数据准备就绪性的 AI 数据准备度量框架 AIDRIN 可量化地评估数据的准备就绪性，并通过指标和可视化报告提高机器学习流程的效率，以便在 AI 应用中做出明智决策。

AI 数据准备检核员 (AIDRIN) 用于评估数据对人工智能的准备情况

AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data  Readiness for AI

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.

通过全面调查和分析超过 120 篇学术论文及 AI 专家的文章，本研究提出了对结构化和非结构化数据集的 AI 数据就绪度 (DRAI) 测量指标的分类法，旨在为 AI 训练的质量和准确性提供新的标准。