AIDRIN 2.0: A Framework to Assess Data Readiness for AI

TL;DR

AIDRIN 2.0 is an enhanced framework for assessing data readiness in AI, focusing on usability and privacy, especially within federated learning environments, demonstrated through a real-world case study.

Contribution

This paper introduces improvements to AIDRIN, including a better user interface and integration with privacy-preserving federated learning frameworks, making data readiness assessment more accessible and privacy-aware.

Findings

Improved UI enhances user experience and accessibility.

Integration with PPFL ensures data privacy in federated settings.

Case study validates practical effectiveness in real-world scenarios.

Abstract

AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy. This paper details enhancements to AIDRIN by focusing on user interface improvements and integration with a privacy-preserving federated learning (PPFL) framework. By refining the UI and enabling smooth integration with decentralized AI pipelines, AIDRIN becomes more accessible and practical for users with varying technical expertise. Integrating with an existing PPFL framework ensures that data readiness and privacy are prioritized in federated learning environments. A case study involving a real-world dataset demonstrates AIDRIN's practical value in identifying data readiness issues that impact AI model performance.