Is your AI smart enough to explain how it thinks and makes decisions?

Photo of Dr. Majid Komeili

Dr. Majid Komeili

Assistant Professor, School of Computer Science, Carleton University

November 7, 2022 10:00 - 11:00

Mackenzie Building, Room ME3380, Carleton University




During the past decade, we have seen a surge in AI-based products and services entering the market. This has raised a lot of concerns about fairness, accountability and transparency of AI. Many of these issues are rooted in lack of understanding of such black-box AI models and how they work and why they make certain decisions. Explainable AI is a field of research that aims to make AI systems more transparent and understandable. In this talk, I will provide an overview of Explainable AI and recent advances in the field. I’ll also discuss several applications in biomedical field..


Dr. Majid Komeili is an Assistant Professor at the School of Computer Science and the Institute for Data Science at Carleton University. He performs fundamental and applied research in machine learning. His research focuses on developing machine learning models with Explainability in mind, and methods that can decipher existing black-box ML models. He is also interested in how ML models can learn to perform new tasks with a limited amount of labeled data. Before joining Carleton, he was a postdoctoral fellow at University of Toronto working jointly with Toronto Rehabilitation Institute and the Vector Institute. He received his Ph.D. in Electrical and Computer Engineering from University of Toronto.

Last updated November 1, 2022