Lesson 1465 of 2244
AI Recommender Radicalization Audits: Trajectory Testing
Recommender systems can drift users toward harmful content — design trajectory audits that test journeys, not just individual recommendations.
Adults & Professionals · Safety & Governance · ~7 min read
The premise
AI can simulate user trajectories through your recommender and flag harmful drift, but harm thresholds need policy ownership.
What AI does well here
- Generate synthetic-persona watch sessions across diverse starting points.
- Cluster trajectory endpoints against a harm taxonomy.
What AI cannot do
- Decide what level of drift constitutes a policy violation.
- Replace a multidisciplinary harm review.
Key terms in this lesson
Practice this safely
Use a real but low-risk workflow from your day. Treat AI as a drafting and organizing layer, then verify the output before anyone relies on it.
- 1Ask AI to explain recommendation trajectory in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "AI Recommender Radicalization Audits: Trajectory Testing" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check rabbit hole against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
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