Mirror, Mirror: Exploring Stereotype Presence Among Top-N Recommendations That May Reach Children
Robin Ungruh, Murtadha Al Nahadi, Maria Soledad Pera. 2025. “Mirror, Mirror: Exploring Stereotype Presence Among Top-N Recommendations That May Reach Children”. In ACM Transactions on Recommender Systems. doi:10.1145/3721987
Abstract
Children form stereotypes by observing stereotypical expressions during childhood, influencing their future beliefs, attitudes, and behavior. These perceptions, often negative, can surface across the many online media platforms that children access, like streaming services and social media. Given that many of the items displayed on these platforms are commonly selected by recommendation algorithms (RAs), it becomes critical to investigate their role in suggesting items that could negatively impact this vulnerable population. We address this concern by conducting an empirical evaluation to gauge the presence of Gender, Race, and Religion stereotypes among the top-10 recommendations generated by a wide range of RAs across two well-known datasets in different domains: Movielens (movies) and GoodReads (books). Results analyses reveal that all RAs frequently recommend stereotypical items. Gender stereotypes are particularly prevalent, appearing in almost every recommendation list and emerging as the most common stereotype. Our results indicate that no algorithm has a consistent tendency towards recommending more stereotypical content; instead, high stereotype presence can be found across recommendation strategies. Outcomes from this work underscore the potential risks that RAs pose to children in perpetuating and reinforcing harmful stereotypes—this prompts reflections on their implications for the design and evaluation of recommender systems.