In the context of the deep penetration of information technologies and services into people’s lives, the issues of control over recommendation systems (hereinafter – RS), which are actively used by social networks and Internet applications for personalized selection and ranking of content for users, are becoming increasingly relevant. The concept of RS operation is based on the preliminary collection of various types and degrees of sensitivity data about the user and their subsequent algorithmic processing in order to provide personalized recommendations. Personalized recommendations selected according to certain methods can create different worldviews for the same users, provoke active actions, etc. Thus, there is a need for a tool to assess the susceptibility of RS to the influences that lead to the bias of recommendation algorithms, on behalf of an external observer.