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Online social networks use AI techniques to automatically infer profiles from users’ shared data. However, these inferences and their effects remain, to a large extent, opaque to the users themselves. We propose a method which raises user awareness about the potential use of their profiles in impactful situations, such as searching for a job or an accommodation. These situations illustrate usage contexts that users might not have anticipated when deciding to share their data. User photographic profiles are described by automatic object detections in profile photos, and associated object ratings in situations. Human ratings of the profiles per situation are also available for training. These data are represented as graph structures which are fed into graph neural networks in order to learn how to automatically rate them. An adaptation of the learning procedure per situation is proposed since the same profile is likely to be …