  "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.
