Start by clarifying the scope: which types of harmful/policy-violating ads (e.g., scams, misinformation, illegal products) and which surfaces (Feed, Stories, Reels). Then define a north star metric that balances harm reduction with business impact, and break it down into input, output, and guardrail metrics. Finally, propose a measurement plan including experiments and long-term tracking.
Pro tip: Emphasize the trade-off between reducing harmful ads and maintaining advertiser experience and revenue; show you understand that over-blocking can harm legitimate businesses and user experience. Also, mention the importance of defining 'harm' clearly and using a combination of automated and human review.
Ask clarifying questions to understand which types of harmful ads are in scope (e.g., scams, counterfeit goods, misinformation) and which platforms/surfaces are prioritized. Define what constitutes a 'harmful' ad and the policy violations.
Establish goals that reduce harm while preserving legitimate advertiser value and user trust. For example, reduce prevalence of harmful ads by X% while maintaining advertiser satisfaction and revenue.
Choose a north star metric like 'prevalence of harmful ads per 10,000 ad impressions' or 'percentage of harmful ads removed proactively.' Break down into input metrics (e.g., detection accuracy, review time), output metrics (e.g., removal rate, appeal rate), and guardrails (e.g., false positive rate, advertiser churn).
Outline how to measure: use human review panels, automated classifiers, and user reports. Suggest A/B tests to measure impact of new detection models or policy changes on harm reduction and business metrics.
Set up dashboards for real-time monitoring, define review cadence, and establish a process for iterating on models and policies. Communicate progress to stakeholders and adjust goals as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.