  A common explanation for negative user impacts of content recommender systems
is misalignment between the platform's objective and user welfare. In this
work, we show that misalignment in the platform's objective is not the only
potential cause of unintended impacts on users: even when the platform's
objective is fully aligned with user welfare, the platform's learning algorithm
can induce negative downstream impacts on users. The source of these user
impacts is that different pieces of content may generate observable user
reactions (feedback information) at different rates; these feedback rates may
correlate with content properties, such as controversiality or demographic
similarity of the creator, that affect the user experience. Since differences
in feedback rates can impact how often the learning algorithm engages with
different content, the learning algorithm may inadvertently promote content
with certain such properties. Using the multi-armed bandit framework with
probabilistic feedback, we examine the relationship between feedback rates and
a learning algorithm's engagement with individual arms for different no-regret
algorithms. We prove that no-regret algorithms can exhibit a wide range of
dependencies: if the feedback rate of an arm increases, some no-regret
algorithms engage with the arm more, some no-regret algorithms engage with the
arm less, and other no-regret algorithms engage with the arm approximately the
same number of times. From a platform design perspective, our results highlight
the importance of looking beyond regret when measuring an algorithm's
performance, and assessing the nature of a learning algorithm's engagement with
different types of content as well as their resulting downstream impacts.
