Product design question
How would you combat misinformation in the newsfeed on COVID-19?
- Meta
- Product design
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Product design under a sensitive, high stakes trust and safety scenario: can you balance free expression, speed, and accuracy in fighting health misinformation.
How to approach it
- State the core tension: acting fast enough to limit harm from viral misinformation while avoiding over removal that damages trust and free expression.
- Identify detection approaches: a combination of automated pattern matching for known false claims and human fact checker review for novel or ambiguous content, since automation alone misses nuance.
- Prioritize labeling and reduced distribution over outright removal for most cases, reserving removal for claims proven to cause imminent physical harm, like false cures.
- Design the response: attach a credible source label and link to authoritative health information directly on flagged posts, and reduce algorithmic distribution rather than always deleting.
- Address scale, since manual fact checking cannot cover every post, so prioritize review capacity toward posts with high predicted reach.
- Define success as reduced reach of confirmed misinformation and, carefully tracked, user trust in the platform's health information, not just removal counts.
What a strong answer includes
- Explicitly names the free expression versus harm reduction tension rather than treating this as a simple removal problem.
- Proposes a graduated response, labeling and reduced distribution before outright removal, showing nuanced judgment.
- Prioritizes review capacity by predicted reach, a practical way to scale limited human fact checking.
Common mistakes
- Proposing blanket automatic removal of anything flagged, ignoring free expression and false positive risk.
- Ignoring the scale problem, assuming every post can be manually fact checked.
Likely follow-up questions
- How would you handle a claim that is contested among experts, not clearly false?
- How would you measure whether labeling actually reduces belief in misinformation?
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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