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.

内容推荐系统的负面用户影响通常被解释为平台目标与用户福利不一致。然而，本文揭示了即使平台目标与用户福利完全一致，学习算法也可能引起用户的负面影响。用户反馈速率的差异可能影响学习算法与不同内容的互动，进而无意中推广具有特定属性的内容。我们的研究结果强调了除了后悔之外，对算法性能的评估还需要关注学习算法与不同类型内容的互动方式及其带来的下游影响。