Adversarial robustness in knowledge graphs

The introduction of false or misleading information into knowledge graphs– the models that power search agents and conversational agents– has serious implications for AI safety, as it allows for misinformation to become embedded into models and spread widely. University of Toronto Professor Ebrahim Bagheri, Canada CIFAR AI Chair Jian Tang and McGill University Professor Benjamin Fung will design machine learning defenses to detect and mitigate adversarial modifications in knowledge graphs. By designing scalable adversarial training and robustness evaluation methodologies, their research will allow for practical deployment of safer knowledge graphs in the real world.