  Automated audits of recommender systems found that blindly following
recommendations leads users to increasingly partisan, conspiratorial, or false
content. At the same time, studies using real user traces suggest that
recommender systems are not the primary driver of attention toward extreme
content; on the contrary, such content is mostly reached through other means,
e.g., other websites. In this paper, we explain the following apparent paradox:
if the recommendation algorithm favors extreme content, why is it not driving
its consumption? With a simple agent-based model where users attribute
different utilities to items in the recommender system, we show through
simulations that the collaborative-filtering nature of recommender systems and
the nicheness of extreme content can resolve the apparent paradox: although
blindly following recommendations would indeed lead users to niche content,
users rarely consume niche content when given the option because it is of low
utility to them, which can lead the recommender system to deamplify such
content. Our results call for a nuanced interpretation of ``algorithmic
amplification'' and highlight the importance of modeling the utility of content
to users when auditing recommender systems. Code available:
https://github.com/epfl-dlab/amplification_paradox.
