AI is everywhere. It's being integrated into the tools we use everyday for work and play. It's being used for peace and it's being used for war. However, AI is currently a black box: we know what goes into training the AI model and we can study the output, but how exactly a model makes its decisions is still a bit of a mystery. How can we trust AI models if we don't understand how they reach their conclusions? During this session, we will explore the topic of Explainable AI and debate whether or not AI systems will ultimately explain their decisions to us, much like human beings do.
Two participants will debate that AI systems will eventually be explainable while the other two participants will debate that AI systems will continue to be a black box. Each debater will be allotted 5 minutes for their opening statements followed by 20 minutes of rebuttals. The debate will close with a Q&A session from the audience.
The debate will be moderated by Amber Mac and will feature Dr. Frank Rudzicz (University of Toronto), Dr. Amber Simpson (Queen's School of Computing), Dr. Pasquale Minervini (University College London), and Dr. Tracy Jenkin (Smith School of Business)!
Dr. Frank Rudzicz is an Associate Professor in Computer Science at the University of Toronto. He is also a Scientist with the International Institute for Surgical Safety at the Li Ka Shing Knowledge Institute at St. Michael’s Hospital; Director of AI, Surgical Safety Technologies; Co-Founder of WinterLight Labs; and a Faculty Member at the Vector Institute for Artificial Intelligence.
An international expert on speech technology for individuals with speech disorders, his research applies natural language processing and machine learning to various aspects of healthcare, including detecting dementia for speech.
Dr. Amber Simpson is the Canada Research Chair in Biomedical Computing and Informatics and an Associate Professor with the School of Computing in the Faculty of Arts and Science and the Department of Biomedical and Molecular Sciences in the Faculty of Health Sciences at Queen’s University.
After receiving a PhD in Computer Science from Queen’s, she joined Venderbilt University as a Research Assistant Professor in Biomedical Engineering at became faculty at the Memorial Sloan Kettering Cancer Centre in New York. She is an American Association of Cancer Research award winner and recipient of the Mihran and Mary Basmajian Award for Excellence in Health Research.
Joining the Queen’s School of Computing in 2019 and specializing in biomedical data science and computer-aided surgery, her research group is focused on developing novel computational strategies for improving human health.fer in modern network systems.
Dr. Pasquale Minervini is a Senior Research Fellow at the University College London (UCL) and a Consultant with the NEC Laboratories Europe GmbH.
His research looks to improve machine learning systems by drawing connections to other fields of AI with a focus on designing representation and deep learning models that are statistically robust and trustworthy, data efficient, verifiable, and explainable, and their applications.
Dr. Tracy Jenkin is an Associate Professor at the Smith School of Business, a Faculty Affiliate at the Vector Institute for Artificial Intelligence, and an Affiliate Member of Ingenuity Labs.
She is a Distinguished Faculty Fellow of MIS and her research explores human-AI collaboration and cognitive processes.
Dr. Jenkin received her PhD in Management (MIS), with a minor in Computer Science, from Queen’s University. She previously worked in the IT and software industry for ten years, working with a broad range of organizations, such as Accenture, Dell and new venture Trilogy Software, and across a diverse set of industries, including telecom, high-tech, financial services, and commodity chemicals.