Survey analysis on phiz

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.

Making CCHS analysis faster and easier to repeat

Mon, 13 Jul 2026 00:00:00 -0400

Public health teams spend an appreciable amount of time responding to requests to analyze CCHS data. These requests often require complex code, and even small changes in scope can mean updating the analysis workflow. As a result, some opportunities to use CCHS data are missed. The work is time-intensive, and it often depends on a small number of experts who understand how to apply the required weighting and bootstrap techniques correctly.

At WDG Public Health, we are reimagining the CCHS analytics process by creating an automated way to analyze the data while maintaining the same technical accuracy. An automated approach would reduce the time required for routine requests and allow teams to reallocate that time to other priorities. The goal is to make the process easier to use, easier to share, and easier to improve through collaboration and an open approach to development.

CCHS Tool

Fri, 06 Feb 2026 00:00:00 -0500