Introduction
The Facebook Live team is facing a critical decision: implement AI content moderation with potential false positives or rely on user reports for content moderation. This trade-off involves balancing the need for real-time content moderation with the risk of erroneously flagging legitimate content. I'll analyze this scenario using a structured approach, considering various stakeholders, metrics, and potential outcomes.
I'll start by asking clarifying questions, then identify the trade-off type, analyze the product, and propose a hypothesis. Following that, I'll define key metrics, design an experiment, plan data analysis, create a decision framework, and finally provide a recommendation with next steps.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Helps understand the baseline and areas for improvement Expected answer: Current system relies heavily on user reports, with some delay in addressing issues Impact on approach: Would influence the urgency of implementing AI moderation
Why it matters: Ensures the solution supports broader company goals Expected answer: Critical for user trust and advertiser confidence Impact on approach: Would justify significant resource allocation
Why it matters: Helps tailor the solution to protect vulnerable users Expected answer: Younger users and certain geographic regions more affected Impact on approach: Would focus on specific user segments for initial rollout
Why it matters: Determines if AI is ready for real-time implementation Expected answer: 85-90% accuracy, with ongoing improvements Impact on approach: Would influence the balance between AI and user reports
Why it matters: Ensures we can support the chosen solution long-term Expected answer: Dedicated AI team available, but limited content review staff Impact on approach: Would lean towards automated solutions with human oversight
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