Personalized AI systems, from recommendation systems to chatbots, are a prevalent method for distributing content to users based on their learned preferences. However, there is growing concern about the adverse effects of these systems, including their potential tendency to expose users to sensitive or harmful material, negatively impacting overall well-being. To address this concern quantitatively, it is necessary to create datasets with relevant sensitivity labels for content, enabling researchers to evaluate personalized systems beyond mere engagement metrics. To this end, we introduce two novel datasets that include a taxonomy of sensitivity labels alongside user-content ratings: one that integrates MovieLens rating data with content warnings from the Does the Dog Die? community ratings website, and another that combines fan-fiction interaction data and user-generated warnings from Archive of Our Own. We conduct comprehensive summary statistical analyses on these datasets and train three distinct recommendation algorithms on each. Our experimental analysis examines how these algorithms either amplify or mitigate the presence of content warnings. This work aims to provide critical insights into whether standard recommendation systems disproportionately highlight sensitive content and offers a robust foundation for future research and the development of more nuanced AI systems that account for content sensitivities.