Unblocking $30M with AI for ads integrity
Risk and training AI in data-sensitive use cases
Problem
30 millions ads are rejected daily for using promotional images, text or video content that doesn't comply with our Advertising Standards. 70% of all ads rejected for these issues are remediable, but only 7% of advertisers actually attempt to fix them. Some issues are more clear-cut, such as explicit nudity, whereas other issues are more ambiguous, such as using comparison images which might 'make people feel bad about their body'.
Creating and uploading ad campaigns is time-consuming and expensive; image galleries can sometimes contain up to 500 images and when an ad is rejected it can be challenging to understand what within the ad (or 500 images) actually caused the issue. How can we empower advertisers to identify and resolve issues with ad creative.
What I did
- Created model architecture, novel eval and labelling criteria
- Created AI agents to review over 7000 LLM-generated responses to benchmark response quality
- Created Meta's first AI integrity playbook to define guardrails for data ingestion, and transparency UX per policy
- Created UX principles for AI
- Created and tested AI response principles for integrity
- Trained and iterated AI models on our internal policies
- Designed E2E UX
Who I worked with
1 Designer 1 Researcher 1 Product Manager 4 Engineers 1 Data Scientist 1 Policy Manager 1 Lawyer 1 Product Marketing Manager
Constraints
- Limited real estate AI is known for it’s (unfortunate) verbosity. We had the space of an error message to explain why a particular image/text/video was violating and needed to be succinct. How to overcome?
- Real-time abuse vector AI ads editing tool could become a new abuse vector. If we allowed unlimited queries, bad actors could abuse this and map our rules to circumvent our policies.
- Policy and legal concerns To avoid legal liability should the model hallucinate, we were unable to give advertisers instructions on how to fix, e.g. Say “Remove this image”. How can we frame guidance to be useful?
- Surface-owner alignment We don’t own the components, or interactions we were proposing and needed to align with the surface-owner team, which was slow- especially as some of the things we were proposing were net-new.
How I did it
Solution
Impact