Lesson 302 of 1550
AI Recommendation Systems: When Engagement Optimization Harms Users
Recommendation AI optimized for engagement can promote harmful content. Designing systems that resist this requires deliberate trade-offs.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2recommendation AI
- 3engagement optimization
- 4harm reduction
Concept cluster
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Section 1
The premise
Pure engagement optimization in recommendation AI predictably promotes harmful content; better systems require explicit harm-reduction trade-offs.
What AI does well here
- Define harm categories explicitly (self-harm content, eating disorders, extremism) and de-promote them
- Trade off engagement for user wellbeing on identified harm vectors
- Maintain human review of edge-case content rather than pure-AI moderation
- Surface metrics tracking both engagement AND user wellbeing
What AI cannot do
- Eliminate all harm without sacrificing some engagement
- Substitute pure-AI moderation for human judgment on novel cases
- Make recommendation systems neutral (they always have values)
Key terms in this lesson
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