Leveraging AI in Canada's Social Response to COVID
Data and AI helping anticipate social needs before they become crises.
Project Overview
Updated March 31, 2023
The Problem
The COVID-19 pandemic has resulted in increased isolation, declining incomes, reduced access to care and other stressful challenges – all impacting Canadian’s social well-being.
Issues such as homelessness, mental health, addictions, domestic violence and community safety have been amplified. Despite the $280 billion spent on the Canadian social services sector every year, more data is needed to follow changes in demand and service provision.
To date, our social responses during the pandemic have been primarily reactionary, rather than planned and proactive. Canada has a robust social safety net to help individuals and communities tackle the challenges, but there are hurdles for people to navigate the complex web of more than 250,000 support services across the country. As a result, many citizens in need fall through the cracks.
How We Are Solving It
Led by HelpSeeker, this project is harnessing the power of artificial intelligence and machine learning to provide decision makers insights that can focus their COVID-19 responses and mitigate its long-term social impacts.
HelpSeeker is partnering with A Way Home Canada, AltaML, Corsac Technologies, Canada Mortgage and Housing Corporation, the Faculty of Social Work at the University of Calgary and the Canadian Observatory on Homelessness at York University.
The project team is leveraging HelpSeeker’s dataset of more than 134,000 services across Canada, as well as hundreds of thousands of interactions from users with these services. A new data pipeline will bring together additional real-time data from governments, public agencies and community groups. This integration of information will provide valuable insights and help target funding and programming related to homelessness, domestic violence and suicide.
The goal is to help government and community leaders anticipate the social needs of Canadians before they become crises. The project team plans to take their learnings from this pilot and support other key areas such as mental health, housing needs and community safety.
The Result
This project partnered Canada’s top social researchers and machine learning experts to develop the InnSoTech predictive algorithm to better anticipate occurrences of homelessness, suicide and domestic violence. The artificial intelligence powered platform provides real-time data and insights to predict community and social support needs before they become crises for evidenced-based decision making. InnSoTech is being utilized by multiple cities across Alberta to enumerate homelessness.
Project Lead
Project Partners
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“With the Digital Supercluster's support, we were able to land some significant federal contracts and build a machine learning team. They have also guided us in developing a five-year plan and 24-month targets. It’s not just about the investment, it’s about a meaningful partnership. You can’t put a price tag on these relationships.”
CEO and Founder, HelpSeeker Inc.