Product design question
How will you solve the problem of misinformation on FB?
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What this question tests
Tests product design on a high-stakes, high-scrutiny trust and safety problem, balancing free expression against harm reduction at platform scale.
How to approach it
- Clarify scope: focus on virally spreading false claims like health and elections misinformation, not all disputed opinion content.
- Identify the mechanism: misinformation spreads fastest through reshares and groups before fact-checks catch up, so detection speed matters as much as accuracy.
- Propose a layered design: classifiers flag likely-false claims for expedited third-party fact-check review, reduced distribution while under review, and a visible label with context once confirmed.
- Address the trade-off: label and reduce reach rather than delete outright for borderline cases, reserving removal for confirmed high-harm categories like crisis health misinformation.
- Address scale: prioritize fact-checking capacity on high-reach posts, verified pages and viral chains, rather than spreading review thin everywhere.
- Define success: reduction in reshares of confirmed-false content after labeling, and time from initial spread to first fact-check label.
What a strong answer includes
- Distinguishes reduce-and-label from outright removal, reflecting the real trade-off Meta has publicly wrestled with.
- Prioritizes speed of initial detection as equally important to accuracy, since virality outruns slow review.
- Focuses review capacity on high-reach content first, a realistic resource-allocation call at platform scale.
- Names a measurable proxy, time to first label, that captures the core problem of speed, not just labeling volume.
Common mistakes
- Proposing blanket removal of false content without addressing free-expression and error-rate trade-offs.
- Ignoring that speed of spread, not just detection accuracy, is the core challenge at this scale.
Likely follow-up questions
- How would you handle a claim true in one context but misleading in another?
- How would you measure false positives, real content mistakenly flagged?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop