Responsible AI Roadmap Webinar: Responsible AI Policy Language

A clip from our AI Safe Sandbox webinar (September 2025), which we also ran as the Responsible AI Roadmap webinar. Recorded in 2025: AI tools’ settings and features have changed since.

Examples of what AI policy language looks like in practice, from a one-line disclosure to a rule for checking facts.

Key takeaways

• Transparency: we end our online posts with a line saying we used AI for polish, not purpose.
• Hallucinations: any factual claim generated by AI must be verified against at least two independent, reputable sources before it goes into a final output.
• A pinned red-amber-green sheet at a project manager’s desk keeps the data rules in view while people work.

Try this with your team

Draft your own one-line AI disclosure. Where would it appear, and would your donors or clients find it honest?

Going further

See how your policy language holds up with the free AI Policy Health Check.

Transcript

Lightly edited from YouTube’s automatic captions for readability. Wording and order are the speakers’ own.

We start with a policy that ensures transparency. Something we do at the end of any of our posts online for marketing, in the posted media file or the blog post, whatever it is: we include a line that says we used AI for polish, not purpose.

For heading off hallucinations, we might have specifications on our AI policy for conducting AI research. For example: any factual claim generated by AI must be verified against at least two independent, reputable sources before inclusion in final outputs.

We might also create a resource sheet that can be pinned up at a project manager’s desk, something like the green, amber, red, just to keep something handy so that you’re reminding yourself as a project manager what you can or can’t include if you’re doing research using an AI chatbot.