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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., Comment: Authors listed in alphabetical order. Accept as poster at AISTATS 2024

Computer science (CS) has historically struggled with issues related to diversity, equity, and inclusion (DEI). Based on how these issues were affecting PhD students in our department, we identified required DEI education for PhD students as a potentially high-impact approach to improving the PhD student experience in our program. Given that no existing curriculum met the desired criteria, we (PhD students) - along with many others at our school - developed and implemented CS-JEDI: Justice, Equity, Diversity, and Inclusion in Computer Science. CS-JEDI is a 6-week DEI curriculum that is now taken by all first-year PhD students in our department. This paper covers CS-JEDI's motivation and goals; describes how its evidence-based curriculum is tailored to these goals and to the CS PhD context; and gives a data-driven evaluation of the extent to which CS-JEDI's first offering, in Spring 2022, achieved these goals.