For a local health department, the hard part of AI usually isn’t the technology. It’s knowing where to start: what to read first, what legal exposure to worry about, how to evaluate a tool someone on staff wants to try, what skills your team actually needs.

This is a reading list in the order most departments move through it: policy, then legal risk, then tool evaluation, then skill-building, then real-world examples. Bookmark it, or work through it top to bottom.

We built this list because most AI-in-public-health talks stop at “adopt a framework” and never get further. A conversation about ethics and governance matters, but it doesn’t actually matter if your department isn’t using AI yet, and plenty of departments aren’t. So use this list to get the policy, legal, and governance conversation handled on your own time, so that when you do sit down with AI, or with us, the conversation can be about what you’re actually trying to get off your plate.

Start here

Two places to begin:

• Treat AI Like an InternThe mental model we use most often when explaining AI to public health teams.
• Free AI Policy WorkbookA downloadable workbook to help you draft your own policy.

Write the policy first

Before anyone in your department uses AI for real work, you need a policy:

• 12 Things Your AI for Public Health Policy NeedsThe elements a real policy has to cover.
• Free AI Policy WorkbookWalk through it step by step.
• Case Study: A Local Health Department PolicySee what one actually looks like in practice.
• Building AI Readiness in Public HealthNACCHO’s webinar series on getting your department ready.

Know the legal exposure

Once you know what the policy needs to cover, look at the legal risk underneath it:

• Generative AI and Health Departments: Legal Considerations and RisksNetwork for Public Health Law’s rundown of the risks.
• AI Legal ConsiderationsOur own breakdown for public health.
• AI and Public Health: Opportunities and ChallengesA broader look at both sides.

Evaluate a tool

Somebody on your team will want to try a specific AI tool. Here’s how to check whether it’s safe:

• Seven Layers of AI SafetyOur framework for evaluating any AI tool before you adopt it.
• CDC AI Strategy, FY2026 to FY2030Where CDC is headed, for context.

Build the skill

Policy and legal groundwork only get you so far. Staff also need to know how to use AI well:

• M-A-P: Map, Add Context, PromptOur core framework for using AI well.
• Treat AI Like an InternWorth a second read once you’re using AI regularly, not just considering it.
• Add ContextWhy context is the difference between a useless AI response and a useful one.
• Prompt EngineeringPractical prompting for public health work.
• AOHC Worksheet and SlidesHands-on materials from our AOHC session.

See it working somewhere real

None of this matters if it doesn’t hold up in practice. It already has:

• Sauk County’s AI Work Cited in AJPHA local health department’s AI work recognized alongside NYC, Chicago, and the CDC.
• Applying the Tools and Navigating the PitfallsA Dialogue4Health web forum on real-world use.
• The Role of AI in Advancing Public HealthICF’s take on where AI fits.
• What We Learned From 50 ConversationsPatterns we found after talking to 50 public health professionals about AI.

Governance and ethics

For departments thinking past their own four walls, about what responsible AI governance looks like at a system level:

• When AI Meets Global HealthHarvard Global Health Institute.
• Frameworks for Advancing and Governing Ethical AI in Global HealthA recorded session on governance frameworks.
• The Data Revolution and Population HealthNational Academies project on data and population health.

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