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Tackling Data Bias in Artificial Intelligence


Artificial intelligence (AI) plays an important role in our everyday lives. To use it properly, we must understand the data, which forms the foundation of any AI algorithm.

As government organizations continue to explore the uses of AI, we must tackle the inherent data biases in AI systems, applications and business processes. We must ensure that the data powering AI systems is accurate and reliable and that it does not inadvertently harm specific groups of people.

Join the event to continue the ongoing discussion around the risks of data bias. The focus this time will be on preventing and spotting algorithmic biases, removing erroneous assumptions in AI systems, and exploring the legal ramifications. Participants will learn how to develop robust data governance structures that foster responsible use and development of AI based on the shared values of diversity and inclusion.

Master of Ceremonies

Erica Vezeau, Director General, Digital Academy, Canada School of Public Service


  • Shingai Manjengwa, Chief Executive Officer and Founder, Fireside Analytics, and Director, Technical Education, Vector Institute for Artificial Intelligence
  • Brent Barron, Director, Strategic Projects, Knowledge Mobilization, CIFAR
  • Christopher Allison, Chief Data Officer, Public Health Agency of Canada
  • Sevgui Erman, Director, Chief Data Scientist, Statistics Canada
  • Karen Eltis, Law Professor, University of Ottawa Centre for Law, Technology and Society


Ima Okonny, Chief Data Officer, Employment and Social Development Canada

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