Moderation Policy

OurManifesto.uk is a public political-speech platform. We intervene in user-submitted text on a single, narrow basis: UK law. We do not police tone, civility, or any party- political line, and we do not down-rank, hide, or rewrite content for being unpopular, controversial, or rude.

What we block

Every string a user submits — comments, manifesto policies, party names and descriptions, profile bios and display names, feedback, and the optional reason field on a flag — is checked against the same five categories before it is saved:

What we do not block

If the AI is unsure, the call is escalated to a human admin and the content is held in a pending state until reviewed. We err on the side of letting speech through.

Three-strike suspension

If three of a user's submissions are blocked under the categories above within any rolling 30-day window, their posting permission is suspended pending appeal. Reading the site, downloading their data, and cancelling subscriptions is unaffected. A successful appeal voids the underlying strike and lifts the suspension automatically.

How to appeal

Every block carries a decision ID. Go to Settings → Moderation history to see your decisions and submit an appeal. A human admin will review within 7 days. We will email you (using your account email) when an appeal is resolved.

How the AI works

We use OpenAI's API as a classifier. Each submission is sent — with no identifying metadata about you beyond an internal user ID — and the model returns one of pass, borderline or block with a category and a short rationale. The model's decisions are recorded so admins can audit them and so repeated overturns calibrate future decisions. We do not use your submissions to train any third-party model. See our Privacy Policy for the full data-processing detail.

Transparency

The full list of moderation categories, the verbatim system prompt, and the appeals workflow are documented in the project repository. If you believe a block was wrong, please appeal it — admin overturns directly improve the AI's future calls via a learned-example feedback loop.