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

Wed, 19 Aug 2026 00:00:00 -0400

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.

Clustering on phiz

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