Danielle Goldfarb

Danielle Goldfarb is a Senior Fellow at the Centre for International Governance Innovation and the Munk School of Global Affairs & Public Policy, and a Distinguished Fellow at the Asia Pacific Foundation of Canada. She serves as Lecturer in the Master of Global Affairs program at the University of Toronto where she teaches GLA2043H - Topics in the Digital World III: Real-Time Data and AI for Global Intelligence. Goldfarb co-directs the Canadian Artificial Intelligence Adoption Initiative, a collaboration between the University of Waterloo, the Centre for International Governance Innovation, and the Centre for the Study of Living Standards.

Goldfarb is an expert on trade, real-time data, economics, and public policy with nearly 100 policy papers written for Canadian and US think tanks. Her research focuses on digital trade, AI applications in economic forecasting, and data collection methodologies. She examines how advances in digitization and artificial intelligence are transforming what can be traded and how, with particular emphasis on Canada's digital services exports which have grown nearly four times faster than goods exports since 2005.

Her recent publications analyze Canada's underutilized digital trade opportunities, particularly in AI-enabled services, where Canada's share of global digital services exports remains below three percent despite significant growth potential. Goldfarb has contributed to the 2025 International AI Safety Report and regularly hosts the 'New Tools of the Economists' Trade' series for economic associations.

Goldfarb's work shows consistent focus on practical applications of real-time data and AI for economic intelligence, with publications spanning digital trade policy, pandemic response strategies, and geopolitical analysis. Her research demonstrates how digital services now comprise 62 percent of Canada's commercial services exports and account for over one in ten of Canada's total exports of goods and services.

As an educator and thought leader, Goldfarb bridges academic research and practical policy applications, connecting data science with economic policy through her teaching, publications, and public speaking engagements including her TEDx talk 'The Smartest Way to Predict the Future' which addresses limitations in current data collection methodologies.