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

TL;DR

AIDRIN is a comprehensive framework that quantifies and visualizes data readiness for AI, addressing quality, fairness, privacy, and FAIR principles to improve AI model effectiveness.

Contribution

This paper introduces AIDRIN, the first framework that assesses data readiness for AI using both traditional and AI-specific data quality metrics.

Findings

AIDRIN effectively evaluates data quality and readiness for AI.

The framework highlights areas needing data improvement before model training.

AIDRIN enhances decision-making in data preparation for AI applications.

Abstract

"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.