AIDRIN 2.0: A Framework to Assess Data Readiness for AI
This work addresses data readiness challenges for AI practitioners, particularly in federated learning environments, but it is incremental as it builds on an existing framework.
The paper tackles the problem of assessing data readiness for AI applications by enhancing the AIDRIN framework with user interface improvements and integration with a privacy-preserving federated learning framework, resulting in increased accessibility and practical utility for users.
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.