Start with policies the movement has already published. The four below show a useful range of choices, from values to operating rules. They also span a real range of risk appetite, from cautious to bold.
Four policies from the movement
Stray Dog Institute (full policy PDF) — the values-led model. Each tool category gets a plain discussion of the considerations, then an explicit “our commitment” statement. It has the movement’s clearest position on photorealistic AI imagery: where an image serves as documentary evidence of what animals endure, it should be real — AI-generated substitutes can erode the public trust that investigators took great personal risk to earn. As a funder, SDI also commits to never weighing a grantee’s openness to AI in funding decisions.
Faunalytics — the tool-by-tool model. Organized by category (language models, image generators, machine learning, crawlers), each with a short explainer of how the technology works before the rules. Notable calls: internal drafting is broadly fine while external AI use is narrow and always labeled, and they deliberately allow AI crawlers to index their research, using language models as another channel for reaching people.
Animal Charity Evaluators — the operational model. The most rule-like of the four: two confidentiality tiers (sensitive material requires training-disabled modes; strictly confidential material never enters AI tools), connectors prohibited unless approved, consent required before AI note-takers join calls with external participants, and a commitment that belongs in any staff conversation: AI gains go to increased output for the mission, not to cutting staff.
Bryant Research — the high-trust model, and the boldest of the four. This research consultancy for the animal protection movement names its own bet directly: an overly restrictive AI policy threatens the organization as much as an overly permissive one. Staff are expected to try AI in every part of their work, and no manager sign-off is needed for any tool except photorealistic AI images, video, or audio in client-facing work. Client communications and survey or interview data are the only hard no for consumer AI tools. Most movement organizations will want more guardrails than this. Read it as the far end of the range.
Read all four and notice which choices fit your organization and which you would adapt. Those reactions are the start of your own policy.
What they share
Across these and the broader nonprofit policies we have reviewed, the same building blocks recur:
- An approved-tools list — which tools, on which accounts (organizational, not personal), and who approves additions.
- Data rules — a simple sensitivity classification, with supporter records, donor information, personnel matters, and anything legally sensitive in the “never paste into consumer tools” tier.
- Human review — anything published or high-stakes gets human review; accountability stays with the person, not the tool.
- Disclosure norms — when to say AI was involved, especially for public content and imagery.
- A named owner and review date — every policy above states who maintains it and how often it is revisited, because tools and terms change fast.
General-sector templates
For a document you can adapt, these options are free and current:
- NTEN’s AI resource hub — includes two free, directly downloadable policy templates, one from NTEN and one from ANB Advisory Group.
- Charity Excellence — a free UK-flavored template for small organizations; one of the few that addresses environmental impact.
- AI Impact Hub — Kyle Behrend’s nonprofit AI policy template and maturity model, designed to be customized in an afternoon (free, requires an email address).
Use each example as source material. Another organization’s policy reflects its risk tolerance, tools, and data. Write the rules that fit your own work for animals.
Go deeper: Create your AI policy · Common concerns about AI
