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  • April 20, 2022 from 4:00pm to 5:00pm


How can machine learning improve population health effectiveness, enhance health system efficiency, and support public health decision-making? What can we do to address the impact, governance, ethics, and accountability of these automated decision-making technologies? To explore these questions, the Data Science Interdisciplinary Research Cluster at the University of Toronto’s Dalla Lana School of Public Health invites Statistics Canada to discuss the responsible development of automated processes and provide guidance on the ethical use and implementation of machine learning.

With the increasing use of machine learning across multiple research areas and industries, frameworks to guide the responsible use of machine learning are needed more than ever. As a result, Statistics Canada has developed a Framework for Responsible Machine Learning Processes.

Statistics Canada will present its responsible machine learning framework, followed by a panel discussion on what this framework means for machine learning technologies and applications in population health and health system research. Register to join the conversation.


Deirdre Hennessy is a senior research analyst with the Health Analysis Division in Statistics Canada. She has a PhD in Epidemiology from the University of Calgary in critical care health services research and has completed post-doctoral training in population health modelling at Statistics Canada and the Ottawa Hospital Research Institute. The primary focus of her research has been the development of microsimulation models of chronic disease. More recently she has been involved in infectious disease modelling of COVID-19 to inform personal protective equipment procurement. Her other interests include open science and reproducibility, especially the use of the data science approach to better document complex models and communicate the results of modelling studies.

Mohammed Haddou is unit head in the Methods, Quality, and Research section within the Data Science Division at Statistics Canada. He holds a PhD in Statistics and manages a group of data scientists and numerous data science projects in various areas such as survey sampling, statistical learning, machine learning, data analytics, natural language processing and responsible AI.


Nancy Ondrusek is the Director, Research and Ethics Services at Public Health Ontario, leading the development, implementation, evaluation and management of research and ethics policies, practices, products and services. She is an Adjunct Lecturer at the Dalla Lana School of Public Health and member of the Joint Centre for Bioethics, University of Toronto.

Shion Guha is an Assistant Professor in the Faculty of Information at the University of Toronto. He has helped develop undergraduate and graduate data science programs, including the Faculty of Information’s human-centered data science program. He is co-author of Human-Centered Data Science: An Introduction.

Lief Pagalan is a PhD Candidate at the Dalla Lana School of Public Health, University of Toronto, and a Graduate Fellow at the Schwartz Reisman Institute for Technology and Society. Their research focuses on socially equitable and algorithmically fair machine learning applications for population health.


Laura Rosella, Associate Professor, Dalla Lana School of Public Health, University of Toronto, and Scientific Director of the Population Health Analytics Lab.

This event is presented by Data Science Interdisciplinary Research Cluster at the University of Toronto’s Dalla Lana School of Public Health.