AIDRIN 2.0: A Framework to Assess Data Readiness for AI

• URL: http://arxiv.org/abs/2505.18213v2
• Date: Wed, 25 Jun 2025 01:49:52 GMT
• Title: AIDRIN 2.0: A Framework to Assess Data Readiness for AI
• Authors: Kaveen Hiniduma, Dylan Ryan, Suren Byna, Jean Luca Bez, Ravi Madduri,
• Abstract summary: AIDRIN is a framework to evaluate and improve data preparedness for AI applications.<n>It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy.<n>This paper focuses on user interface improvements and integration with a privacy-preserving federated learning framework.
• Score: 0.7972490974330477
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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