Towards Trustworthy AI: Data pipeline preparation, AI Explainability and Interpretability (AIX) Integration, Federated Learning Implementation, and Advanced Security Solutions

Description :

Given that data management lies at the heart of creating reliable AI systems, trustworthy data management involves processes that guarantee the reliability and credibility of data throughout its lifecycle, ensuring high-quality data essential for training ethical AI models. AI Explainability (AIX) is a set of procedures that allow human users to comprehend and trust the results created by AI systems. Integrating AIX increases accountability and trust by enabling stakeholders to understand how AI systems make decisions. Federated Learning (FL) is a promising framework for distributed machine learning that ensures privacy by training models locally without sharing data. Additionally, Homomorphic Encryption (HE) provides a modern and reliable way to protect user privacy by allowing computation over encrypted data.

Titulaire :
CHAMOUN Maroun

Contact USJ :
maroun.chamoun@usj.edu.lb

Chercheur(s) :
M. Khalil HARISS

Projet présenté au CR, le : 01/09/2024

Projet achevé auprès du CR : 31/08/2027