Your manager at Meta asks about Rights Manager: should we implement more content detection options with higher false positives or improve existing detection accuracy?

Introduction

The trade-off we're examining today is whether Meta should implement more content detection options for Rights Manager with higher false positives or focus on improving the accuracy of existing detection methods. This scenario involves balancing the need for comprehensive content protection with the risk of over-flagging legitimate content. I'll approach this analysis by first clarifying key aspects of the situation, then diving into the product understanding, metrics, and experimentation before providing a recommendation.

Step 1

Clarifying Questions (3 minutes)

• Based on recent platform changes, I'm thinking Rights Manager might be facing new challenges with content identification. Could you provide context on any recent updates to the content ecosystem that might be driving this consideration?

Why it matters: Helps understand the urgency and scope of the problem Expected answer: Increase in short-form video content or new content types Impact on approach: Would influence the focus of detection improvements

• Considering our revenue model, I assume Rights Manager impacts our relationships with major content creators and publishers. How critical is Rights Manager to our current business priorities and partner relationships?

Why it matters: Aligns solution with business objectives Expected answer: High priority, directly impacts key partnerships and revenue Impact on approach: Would justify more resources and a faster timeline

• Looking at user behavior, I'm curious about the current false positive rate and its impact. Can you share any data on user complaints or content disputes related to false positives?

Why it matters: Helps quantify the current user experience issues Expected answer: Moderate increase in disputes, affecting user satisfaction Impact on approach: Would influence the balance between detection expansion and accuracy improvement

• Regarding technical feasibility, I'm wondering about our current AI capabilities. How advanced is our machine learning model for content detection, and what's the potential for significant accuracy improvements?

Why it matters: Determines the realistic options for improving accuracy Expected answer: Moderate AI capabilities with room for improvement Impact on approach: Would guide the focus on AI development vs. expanding detection options

• Considering resource allocation, how does our current team capacity align with the potential scope of either expanding detection or improving accuracy?

Why it matters: Ensures the recommended approach is feasible with current resources Expected answer: Limited additional capacity, need to prioritize efforts Impact on approach: Would influence the scale and timeline of the proposed solution