AIDRIN: AI Data Readiness Inspector

AIDRIN (AI Data Readiness Inspector) is an open-source framework that helps researchers, developers, and organizations assess how ready their datasets are for AI model training.
It provides a structured evaluation across key dimensions that determine the success, reliability, and ethical integrity of AI systems.

Overview​

AIDRIN offers both a user-friendly interface and a programmatic API, allowing users to upload datasets or connect to data sources for automated readiness assessments.

Each dataset is evaluated across six core pillars of AI data readiness:

1. Data Quality
2. Data Understandability & Usability
3. Data Structure and Organization
4. Impact of Data on AI Outcomes
5. Fairness & Bias
6. Data Governance

Each pillar uses established metrics drawn from academic and industry standards, providing users with detailed diagnostic insights before model development begins.

In addition, users can extend AIDRIN by defining their own metrics and automated remedies, allowing custom evaluation and correction of dataset readiness challenges across different domains.

Federated Learning Integration​

AIDRIN integrates with privacy-preserving federated learning workflows via the APPFL framework.
This enables organizations to evaluate data readiness across distributed environments without moving or exposing sensitive data.

Learn more about this integration in the official APPFL documentation.

Open Source​

AIDRIN is open source and actively maintained.
You can install, explore the source code, contribute, or report issues on GitHub

Publications​