# AIDRIN 2.0: A Framework to Assess Data Readiness for AI

arXiv: https://arxiv.org/abs/2505.18213 · HTML: https://mlsanity.com/paper/2505.18213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 5 of 5)

- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework](https://mlsanity.com/paper/2409.11585/) (2024)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI](https://mlsanity.com/paper/2406.19256/) (2024)
- [APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service](https://mlsanity.com/paper/2308.08786/) (2023)

## Built on by (most cited first, 2 of 2)

- [SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data](https://mlsanity.com/paper/2604.26645/) (2026)
- [Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration](https://mlsanity.com/paper/2606.28856/) (2026)

## Abstract (arXiv)

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.