4 papers
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha +2
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and secur…
AIDRIN 2.0: A Framework to Assess Data Readiness for AI
Kaveen Hiniduma, Dylan Ryan, Suren Byna +2
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 qual…
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
Kaveen Hiniduma, Suren Byna, Jean Luca Bez +1
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models train…
Data Readiness for AI: A 360-Degree Survey
Kaveen Hiniduma, Suren Byna, Jean Luca Bez
Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evalu…