Explore the impact of popularity and demographic biases in recommender systems through this conference talk from FAT* 2018. Delve into the core question of ensuring recommendation algorithms work effectively for all users. Examine various evaluation strategies, datasets, and algorithms used to assess and address biases. Learn about initial findings related to profile size, resampling techniques, and methods to mitigate popularity bias. Understand the limitations of current approaches and discover upcoming workshops in this field. Gain insights into systematic differences in recommender systems and their implications for fairness and effectiveness across diverse user groups.