25 au 27 février, 2026
Montréal, Canada
Explainable AI (XAI): Where to Begin
Black-box models fall short when decisions demand clarity and trust. This session shows how Explainable AI make troubleshooting easier and reveals hidden patterns in data. From making financial risk models more transparent to helping retailers see what drives sales, we’ll explore real-world examples and focus on hands-on skills for building understandable models, including the use of Shapley values.
Tamires Marcal
McGill
I’m a Computational Neuroscientist and an AI Researcher with a decade of experience bridging brain science and machine learning. With a strong background in science communication, industry collaboration and open science, I’m dedicated to building tools that bring research closer to real-world solutions. I am currently pursuing a PhD at the McGill Centre for Integrative Neuroscience and Mila – Quebec Artificial Intelligence Institute.
