Explore fairness-aware recommendation systems in this comprehensive tutorial from FAT*2020. Delve into the intricacies of recommendation algorithms, stakeholder concerns, and quality of service issues. Learn about diversity in recommendations, individual fairness, and system monitoring. Gain hands-on experience with librec-auto, a tool for experimenting with fairness-aware recommendations. Discover methodological approaches and parameter sensitivity in recommendation systems. Engage with experts Robin Burke and Masoud Mansoury as they present their collaborative work on fairness in recommender systems.