The slides from the workshop and a curated set of resources to take away.
A 10–15 minute expert provocation for the Food Ethics Council, 9 June 2026. Delivered by Tom Watson, The Good Ship.
You don't need to start from a blank page. These are strong current examples, most explicitly designed to be adapted.
The Scottish Council for Voluntary Organisations published its AI guidelines (v1.0, October 2025) and, unusually, a companion piece explaining how it produced them — the process, the debates, the choices. Useful both as a model and as a worked example of developing a policy in the open, which is exactly what FEC is doing here.
Released March 2026 — directly relevant to a food-systems room. Built on human accountability (AI can't be an author), mandatory disclosure of tool, model and version, rigorous fact-checking, and a ban on AI-generated figures.
A thorough, current guide: where AI genuinely helps by function, the organisational questions (risk, disclosure, data, governance, environmental footprint), and dozens of real examples.
A free, guided tool that walks you through a short Q&A and drafts a custom AI policy covering governance, privacy, risk and ethics. Not a set-and-forget approach, but useful for thinking things through.
Why naming tools in documents doesn't age well — and what to anchor your policy to instead.
The London Office of Technology & Innovation's staff one-pager names Bard (since renamed), "Windows 365 Copilot — soon," Dall-E and Stable Diffusion. Two years on, much of that is out of date. But its six rules — never upload residents' private data, reference AI use, don't let it make decisions, stay accountable for everything it produces — are actually solid.
Anchor it to principles, not products. A list of tools is out of date the moment you publish it; a list of principles is not.
Ways to hold AI choices to account — and some open infrastructure worth knowing about.
A tool for weighing AI models against seven lenses: quality, capability, speed, cost, sustainability, transparency and privacy. A ready-made set of questions to hold any AI choice up to, in a policy or a breakout. Built by The Good Ship.
An open-source project (from Data for Action) that uses AI to parse reports and pull their recommendations into one searchable place, so the sector builds on past work instead of starting from scratch.
How to #QuitBigAI for smaller, local, open models. Another way to explore different models and their functions if Bearing doesn't hit the spot.
An open-source tool developed by CodeCarbon for estimating the energy consumption and environmental footprint when using generative AI models. Includes a calculator to explore the impact evaluation methodology.
A European AI platform that runs on renewable energy, keeps your data in the EU, and shows you exactly what every conversation costs.
A provocation works better with proof. These are real uses of AI in nature and conservation — several from Wildlife Trusts — that show the upside while, at their best, modelling care.
Through Nesta's AI for Good programme, with NatureScot, the Scottish Wildlife Trust worked with Edinburgh firm Space Intelligence to interpret large volumes of satellite radar and optical data — mapping the remaining fragments of natural woodland along rivers to plan wildlife corridors and nature recovery.
Conservationists are using AI-powered cameras to detect invasive stoats, which prey on the eggs and chicks of native ground-nesting birds, as part of efforts to eradicate them and protect those species.
A project combining very high-resolution satellite imagery, machine learning and citizen science to produce some of the most detailed habitat maps of their kind — accurate enough for ecologists and DEFRA to rely on. Local volunteers' field surveys train the model: people aren't at the end of the chain, they're teaching it.
Open-source SpeciesNet identifies wildlife across millions of camera-trap images. Bioacoustic models pick out species from soundscapes. AI teams predict deforestation before it happens.