"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。