I heard about the Scope AI Thon only two weeks into the fall semester of the Master of Management and Analytics (MMA) program. At the time, I imagined it would be another fast-paced hackathon focused on coding, dashboards, and technical challenges. Instead, it turned out to be something very different.
The Scope AI Thon is the first applied artificial intelligence challenge created in support of Centraide of Greater Montreal and its network of community organizations. Co-organized by Centraide and the 海角社区 MMA Advisory Council, in partnership with Techaide, the event brought students together for two intensive working sessions on November 14 and 21.
Instead of building models, teams were tasked with designing a strategic AI roadmap to address complex social issues identified by the community sector. From the first session, it was clear that participants were encouraged to slow down, think deeply and define solutions responsibly. Supported by sponsors including Intact Lab, McKesson Canada, Databricks, and MIA Innovation, the Scope AI Thon offered MMA students a structured way to explore how analytics and AI can address social issues without writing a single line of code.
Understanding the Scope
Participants were introduced to three broad problem areas connected to Centraide鈥檚 work across Montreal: accessibility to community services for vulnerable populations, digital inclusion communication for Greater Montreal and urban insights for social planning. Unlike traditional competitions that provide tidy datasets or fully defined targets, teams worked with high-level briefs shaped by real-world complexity and unanswered questions.
At first, this felt unfamiliar. Many students are used to solving well-structured problems. Here, we were pushed to identify what information mattered, which assumptions could create harm and how much real organizations can support in practice.
One mentor from Google reminded us that real-world problems rarely arrive neatly packaged. Another mentor then asked a question that guided our thinking for the rest of the challenge, 鈥淒ata is biased and we are biased, so you need to design in a way that makes the system safer than you are.鈥�
That advice shifted the experience. It reminded us that scoping was not the preliminary step before the 鈥渞eal work鈥�. It was the work 鈥� where feasibility, risk and unintended bias must be addressed before a model could be built.
Advice that shaped our thinking
Throughout the event, mentors from Google, AskA茂a, TD Assurance, Intact, MIA Innovation, and 海角社区 challenged teams to strengthen their reasoning. Discussions focused on ethical data collection, legal limitations, implementation capacity, and the realities of working with social service organizations.
The final presentations on November 21 were evaluated by a judging panel representing technical, business and community expertise. Judges included Daniel Capriles from BDC, Jasbir Kooner from Englobe, Nada Naji from Pratt and Whitney, Angela Lastra from RBC Borealis, Tatiana Lamoureux Gauvin, MMA Program Director, Taha Havakhor, MMA Academic Director, Claudia Santillana from Centraide du Grand Montr茅al, Alena Ziuleva from Table de Quartier, and Marion Felix from MIA Innovation and Maplr.
One judge summarized the spirit of the event, stating: 鈥淲e are here to measure clarity, feasibility, ethics, and potential impact.鈥� Rather than evaluating polished systems, we were assessed on our reasoning and our responsibility as future practitioners.
Understanding the real complexity
The most valuable takeaway was understanding how layered and messy social problems become when examined deeply. We learned that a solution must reflect ethical considerations, real constraints, and the lived context of the communities it aims to support. We had to consider what data organizations should not collect, how bias might arise from early assumptions and how any roadmap must align with the capacity and resources of community partners.
The pitching session felt like an authentic consulting exercise. Teams had to justify each element of our reasoning and be precise about why our roadmap made sense. We could not hide behind code because there was no code. One participant captured the experience clearly when they said, 鈥淩eal data and real problems are messy and complex. I feel like I now understand what real complexity actually looks like.鈥�
Although only one team won, the top three teams, including ours, were invited to continue working with Centraide executives, proving the event was the start of ongoing collaboration.
Impact beyond the event
The iScope AI Thon showed that responsible AI begins long before a model is trained. It begins with careful thought, honest questions, and an understanding of real-world limitations. For the MMA program, it reinforced the importance of ethics and community awareness in analytics education. For students, it offered a clearer view of how to approach problems that affect people and public impact.
The most important lesson was simple: behind every dataset is a person, and behind every model is a choice. If AI is intended to help people and support communities, the thinking must begin long before coding starts. The Scope AI Thon encouraged us to build that mindset, and that may be its most lasting impact.
Photo credit : MADCAT
Master of Management in Analytics (MMA) Program
The 海角社区 Master of Management in Analytics (MMA) degree is a specialized program in the evolving field of analytics and data science with a strong emphasis on applied and experiential learning through our 3 Pillars: Quantitative Methods, Technology Automation and Business Application. The MMA program touches on many foundational topics that comprise Artificial Intelligence (AI), covering key areas such as Language Modeling, Image Recognition, Analytic Visualization and Data Architecture Automation. Through our comprehensive curriculum, students gain practical skills and knowledge essential for tackling real-world challenges in the rapidly advancing domain of AI and analytics.
