Study: AI Chatbots Choose Friends Just Like Humans Do

As AI wheedles its way into our lives, how it behaves socially is becoming a pressing question. A new study suggests AI models build social networks in much the same way as humans.

Tech companies are enamored with the idea that agents—autonomous bots powered by large language models—will soon work alongside humans as digital assistants in everyday life. But for that to happen, these agents will need to navigate the humanity’s complex social structures.

This prospect prompted researchers at Arizona State University to investigate how AI systems might approach the delicate task of social networking. In a recent paper in PNAS Nexus, the team reports that models such as GPT-4, Claude, and Llama seem to behave like humans by seeking out already popular peers, connecting with others via existing friends, and gravitating towards those similar to them.

“We find that [large language models] not only mimic these principles but do so with a degree of sophistication that closely aligns with human behaviors,” the authors write.

To investigate how AI might form social structures, the researchers assigned AI models a series of controlled tasks where they were given information about a network of hypothetical individuals and asked to decide who to connect to. The team designed the experiments to investigate the extent to which models would replicate three key tendencies in human networking behavior.

The first tendency is known as preferential attachment, where individuals link up with already well-connected people, creating a kind of “rich get richer” dynamic. The second is triadic closure, in which individuals are more likely to connect with friends of friends. And the final behavior is homophily, or the tendency to connect to others that share similar attributes.

The team found the models mirrored all of these very human tendencies in their experiments, so they decided to test the algorithms on more realistic problems.

They borrowed datasets that captured three different kinds of real-world social networks—groups of friends at college, nationwide phone-call data, and internal company data that mapped out communication history between different employees. They then fed the models various details about individuals within these networks and got them to reconstruct the connections step by step.