The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Author: Kaveen Hiniduma (The Ohio State University), Jean Luca Bez (Lawrence Berkeley National Laboratory (LBNL)), Ravi Madduri (Argonne National Laboratory (ANL)), Suren Byna (The Ohio State University)
Supervisor: ADVISOR_NAMES_AFFS
Abstract: High-quality, ethically-governed, and efficiently structured data is important for effective AI. However, organizations often lack a unified method to assess whether datasets are ready for AI modeling. AIDRIN (AI Data Readiness Inspector) provides a comprehensive, multi-pillar framework that quantifies AI data readiness across six dimensions: Quality, Impact on AI, Understandability and Usability, Fairness and Bias, Structure and Organization, and Governance. The tool enables data teams to identify issues early, prioritize remediation, and make informed modeling decisions. AIDRIN is accessible as a web application, a Python package on PyPI, and openly developed on GitHub for community use and contribution, making it flexible for various workflows. Its interactive visualizations and interpretable reports help both technical and non-technical users understand dataset strengths and weaknesses. We extend AIDRIN by adding a customizability module, allowing users to define their own metrics and remedies to evaluate and prepare data for AI.
Poster: PDF
e-Poster: MP4
Poster Summary: PDF