Framework for Responsible Machine Learning: Applications for Population Health and Health Systems
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, Statistics Canada
• Mohammed Haddou, Statistics Canada
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.
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