About this session:
Artificial intelligence (AI) is increasingly embedded in labour market data sources and tools used across the LMI ecosystem. As a result, AI-derived insights are being encountered more frequently—often without clear visibility into how they were produced or how they should be interpreted.
While AI can enhance the timeliness and granularity of certain types of labour market information, it also introduces important constraints and risks. Misunderstanding these strengths and limitations can lead to inappropriate comparisons, misplaced confidence in findings, or misuse of data in analysis and decision-making.
Rather than promoting or dismissing AI-enabled data sources, the session focuses on building understanding of how different types of AI systems are currently used in labour market information, and on supporting informed judgment about when and how different forms of LMI should be used.
What you'll learn:
- What is meant by artificial intelligence in an LMI context
- The different types of AI systems and how they are applied in LMI today
- How AI and machine learning are used to collect and process certain types of labour market data, including online job postings
- The key strengths of AI-derived LMI
- Common limitations and risks associated with AI-enabled data
- Practical guidance for interpreting and using different types of LMI appropriately
Register today!
Who should attend?
This webinar is designed for postsecondary leaders and educators developing programs that respond to evolving workforce needs, policymakers and accreditation professionals shaping education and credential frameworks, employers and industry representatives interested in how accreditation supports emerging professions, researchers and analysts studying education–labour market alignment, and students or early-career professionals navigating pathways into new or regulated occupations.
Ken Chatoor
Director of Research and Strategic Foresight
Ken leads the development and implementation of LMIC’s research vision to meet the data needs of Canada’s evolving labour market. He brings a rich background in education, labour market outcomes, and equity research, having published work on topics such as mental health, government service funding, Work-Integrated Learning, and graduate transitions. Before his policy career, Ken conducted biomedical research in spine regeneration and brain cancer therapies. Outside of work, he enjoys travelling, film and pop culture, and spending time with his Shiba Inu, Kobe.
Louise Ferbach
Data Scientist
Louise contributes to the design and deployment of cloud-based analytics pipelines, helping organizations transform complex datasets into actionable insights. Her work supports decision-making through predictive modeling, anomaly detection, and natural language processing.
Louise is a fully certified actuary and holds two master’s degrees in actuarial science, statistics, and finance from ENSAE and the Polytechnic Institute of Paris.
