With increasing automation and the continuous development of machine learning, modernalgorithms are now used in almost all areas to improve and simplify workflows. RecommendationSystems (RS) are one group of these algorithms. They enable automated suggestionsof items based on the interests of the user. In this work, we will focus on the investigationof biases in recommendation systems for news. News Recommendation Systems (NRS)provide a way to suggest targeted news to users according to their needs. As news is theprimary source of information, it is imperative that it is presented fairly and free from bias.Thus, in addition to good recommendations for the user, novelty and diversity should beensured by the NRS. For this purpose, several experiments are conducted with the MINDdataset, which has collected 1,000,000 users’ data on MSN. This work gives an overviewof the different biases in the feedback loop of news recommendation systems. In particular,data and model biases are examined and related to other user biases. The researchshould enable a template for bias modeling. All tests are presented in a Github repositoryhttps://github.com/LaKin314/Investigating_biases_in_News_Rec.