By: CIFAR
3 Aug, 2023
CIFAR AI Insights: Towards a Proportionate and Risk-Based Approach to Federated Data Access in Canada
August 3, 2023
Advice for policy-makers on how to plan, develop and implement federated learning systems to safely advance machine learning for health systems improvement.
This CIFAR AI Insights Policy Brief explores how federated learning (FL) may be implemented. The authors discuss findings from document review, expert interviews, a validation workshop, and a survey of solutions to privacy, ethics and security challenges raised by FL. In evaluating solutions to potential challenges, they focus on a proportionate response to realized risks, specifically the frequency and magnitude of harm caused by ethical, privacy, and security breaches of health data. They discuss the trade-offs between protections and the utility of data for FL and recommend enabling governance models.
AUTHORS
- Tania Bubela BSc (Hons), PhD, JD, FCAHS, FRSC. Professor & Dean, Faculty of Health Sciences, Simon Fraser University.
- Ivan Beschanstnikh BSc, MSc, PhD. Associate Professor, Department of Computer Science, University of British Columbia.
- Regiane Garcia LLB, LLM, PhD. Research Associate, Faculty of Health Sciences, Simon Fraser University.
- Aline Talhouk BA, MSc, PhD. Assistant Professor and Michael Smith Health Research BC Scholar, Department of Obstetrics & Gynecology, Faculty of Medicine, University of British Columbia and British Columbia’s Gynecological Cancer Research Program (OVCARE).
For more information, contact:
Gagan Gill
Program Manager, AI & Society, CIFAR
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