Erlin Gulbenkoglu

Data privacy expert at Silo.AI

In recent AI research and development more attention are directed to building interpretable, anonymous and fair AI systems. Erlin will explain the challenges and best practices of model interpretability, building anonymous models, data minimisation and anonymisation.

Erlin specialises in privacy-enhancing technologies. In her master thesis, she worked on differentially private data analytics and applied differentially private algorithms in the context of learning.

Currently, she is working on developing privacy standards for AI development. She has technical competence and legal knowledge on privacy.

My Sessions

Practical AI ethics


AI will have the biggest impact on our lives for the foreseeable future. We must harness its potential and power Ethically with personal data privacy at the heart. The Practical AI Ethics tract will tackle these challenges and opportunities! Every Artificial Intelligence has Human Knowledge in the Loop Fabio Massimo Zanzotto, University of Rome Tor […]

AI Ethics