McGill University researchers have developed a more energy-efficient method of building AI systems that are better at measuring—and indicating—their own uncertainty. This will help users determine when human oversight is needed, when additional data should be collected and when a model is being asked to work beyond the conditions it was trained for, the researchers said.
“Artificial intelligence systems now play a central role in daily life, from medical diagnosis and content moderation to autonomous driving and AI agents that act on our behalf,” said Mame Diarra Touré, lead author and Ph.D. candidate in the Department of Mathematics and Statistics. “As these systems take on more responsibility, they need to become more trustworthy. They should recognize when they are uncertain, rather than giving confident answers in situations where they may be wrong.”
The research was supervised by David A. Stephens, professor in the Department of Mathematics and Statistics. “Singular Bayesian Neural Networks,” by Mame Diarra Touré and Stephens, was presented at the Forty-Third International Conference on Machine Learning (ICML 2026).
