Responsible AI Guidelines for Public Health
Practical guidance for using AI in ways that are narrow, reviewable, source-aware, and accountable to human judgment.
Public health teams do not need vague promises about AI transformation. They need practical workflows that make repetitive work faster without weakening trust, clarity, privacy, or accountability.
The operating philosophy
Backyard-AI is built around a simple idea: AI should support public health work, not replace professional judgment. The safest use cases are usually narrow, well-scoped, source-aware, and reviewed by humans before important outputs are used or shared.
These guidelines can help teams discuss where AI belongs, where it does not, and what review steps are needed before AI-assisted work is used.
Core principles
AI should support public health work, not replace expert judgment.
Narrow workflows are safer and more useful than broad, ambiguous ones.
Important outputs need visible human review.
Claims should stay within the limits of the source material.
Early pilots should avoid sensitive data whenever possible.
Staff should know what a workflow will do and what it will not do.
Teams should document review steps when outputs may affect public-facing communication, planning, reporting, or decision support.
What Backyard-AI is for
Backyard-AI is designed to support:
Evidence synthesis and summary support
Health communication drafting and review support
Workforce training and teaching support
Reporting and workflow support
Internal knowledge and policy support
Resource navigation and approved-source knowledge assistants
Responsible implementation planning
Default safeguards
Human review
Important outputs should be reviewed by a person who understands the topic, context, audience, and intended use.
No hallucinated citations
If a claim cannot be supported by the available source material, it should be flagged, revised, or removed.
No sensitive data in public demos
Early demos and examples should rely on public, synthetic, de-identified, or explicitly approved materials.
What should not be pasted into a public AI model
Protected health information
Personally identifiable information
Restricted personnel information
Legally protected case information
Confidential organizational records
Unapproved grant, contract, or internal strategy materials
Any data that your organization would not allow in an external web tool
Source boundaries
Factual workflows should use a narrow approved-source list whenever possible.
No overclaiming
AI-assisted outputs should not sound more certain than the evidence supports.
Traceability
When appropriate, teams should keep a record of the source material, prompts, instructions, reviewer, and approved output.
Better early-pilot inputs
Public guidance documents
Synthetic examples
De-identified text
Approved uploaded documents
Public reports
Training scenarios
Sample data created for demonstration
Risk tiers
Lower-risk workflows
Examples: meeting summaries, internal action lists, training handout drafts, brainstorming, plain-language rewrites, and non-sensitive email drafts.
Review expectation: staff review before use.
Review checklist
Moderate-risk workflows
Examples: dashboard narratives, guidance comparisons, public-facing communication drafts, resource guides, grant narratives, and program summaries.
Review expectation: subject-matter review, source check, and approval before sharing.
Before using an AI-assisted output, ask:
Is the workflow using approved source material?
Was any sensitive data included?
Does the output overstate certainty?
Are dates, names, numbers, citations, and contact details correct?
Is the output within the intended scope?
Is the language appropriate for the audience?
Could the output create confusion, stigma, bias, or harm?
Has the right person reviewed and approved it?
Disclosure examples
Internal disclosure option: This draft was prepared with AI assistance and reviewed by a human before use.
Public-facing disclosure option: This material was drafted with AI assistance using approved source material and reviewed by a human prior to release.
Workshop or training disclosure option: This activity includes AI-assisted examples for learning purposes. Outputs should be reviewed before use in real-world settings.
Need help applying responsible AI guidelines to your team?
Backyard-AI can help you turn these principles into practical guidance, review checklists, staff training, risk tiers, and workflow-specific expectations.
Higher-risk workflows
Examples: workflows affecting legal compliance, clinical interpretation, sensitive data handling, regulatory decisions, public alerts, or high-consequence public communication.
Review expectation: do not proceed without explicit organizational approval, appropriate expertise, and a documented risk review.
These guidelines are for general planning and training purposes. They are not legal, clinical, compliance, or procurement advice. Organizations should follow their own policies and consult appropriate experts when working with sensitive data, regulated settings, or high-consequence decisions.