Equity in AI for Public Health: Part 2 – From Clusters to Careful Personas

Wed, 19 Aug 2026 00:00:00 +0000

Plain-language takeaway: Clustering can help public health teams see how needs, strengths, risks, and supports overlap. Equity changes how the data is prepared, how patterns are tested, and whether a pattern should become a persona.

Part 1 explained why equity in AI must guide public health work. It also introduced the four actions in NIST AI RMF 1.0: govern, map, measure, and manage.

The second article illustrates what these commitments look like in practice. It follows the WHY Survey Personas project from survey preparation to human review. The examples given below are drawn from the project’s workflow, but no sensitive findings, subgroup differences, cluster sizes, or draft persona labels are disclosed.

Equity in AI for Public Health: Part 1 – Why Fairness Must Come First

Wed, 05 Aug 2026 00:00:00 +0000

Plain-language takeaway: AI can assist public health teams in discovering patterns within complex information. Equity makes sure these patterns are developed, understood, and used in ways that are fair, safe, and helpful.

Public health teams often need to understand how different aspects of people’s lives are interconnected. Mental health, family support, school experiences, access to services, discrimination, and other factors rarely occur in isolation. However, these connections can be difficult to identify when relevant information is dispersed across numerous survey questions.

Artificial intelligence, often referred to as AI, can be used to solve those problems. This involves computer-based techniques that can detect patterns, organize information, or make predictions. In the field of public health, such tools enable teams to examine large datasets more efficiently.

Yet the fact that a pattern has been found does not mean that it has been understood. A computer cannot judge whether the pattern shows an unmet need, an unfair social issue, a decision made during the design of the survey, or even a data error. Similarly, it cannot evaluate whether presenting the pattern will be beneficial or harmful to a community.

This is why Equity in AI needs to guide the work from the start.

At Wellington-Dufferin-Guelph Public Health (WDGPH), the WHY Survey Personas project is a real-world example. The project searches for patterns in the Wellbeing and Health of Youth (WHY) Survey to help staff see how youth needs, strengths, risks, and supports overlap.

The survey is a joint effort involving WDGPH, the Upper Grand District School Board, and the Wellington Catholic District School Board; for the 2025–26 survey cycle, WDGPH also collaborated with Conseil scolaire catholique MonAvenir, a French-language Catholic school board. It gathers information from students in Grades 4 to 12, parents or guardians, and school staff to help with school and community planning. The student portion of the survey lasts approximately 30 minutes and is available in separate versions for Junior (Grades 4 to 6) and Intermediate/Senior (Grades 7 to 12) students. The survey includes topics such as mental health, relationships, school climate, physical activity, substance use, and equity.

Andres Bravo on phiz

Equity in AI for Public Health: Part 2 – From Clusters to Careful Personas

Equity in AI for Public Health: Part 1 – Why Fairness Must Come First