We envision a human-AI ecosystem in which AI-enabled devices act as proxies of humans and try to learn collectively a model. To this aim, techniques for decentralized learning are employed. Each device will learn a local model that needs to be combined with the models learned by the other nodes, in order to improve both the local and global knowledge. The challenge of doing so in a fully decentralized AI system entails understanding how to compose models coming from heterogeneous sources and, in the case of potentially untrustworthy nodes, decide who can be trusted and why. To tackle this challenge, in this microproject we focus on the specific scenario of model “gossiping” for accomplishing a decentralized learning task and we study what models emerge from the combination of local models, where combination takes into account the social relationships between the nodes (humans associated with the AI). We will use synthetic graphs to represent social relationships and large-scale simulation for performance evaluation.