With the progress of artiﬁcial intelligence and the emergence of global online communities, humans and machines are increasingly participating in mixed collectives in which they can help or hinder each other. Human societies have had thousands of years to consolidate the social norms that promote cooperation; but mixed collectives often struggle to articulate the norms which hold when humans coexist with machines. In ﬁve studies involving 7,917 individuals, we document the way people treat machines differently than humans in a stylized society of beneﬁciaries, helpers, punishers, and trustors. We show that a different amount of trust is gained by helpers and punishers when they follow norms over not doing so. We also demonstrate that the trust-gain of norm-followers is associated with trustors’ assessment about the consensual nature of cooperative norms over helping and punishing. Lastly, we establish that, under certain conditions, informing trustors about the norm-consensus over helping tends to decrease the differential treatment of both machines and people interacting with them. These results allow us to anticipate how humans may develop cooperative norms for human-machine collectives, speciﬁcally, by relying on already extant norms in human-only groups. We also demonstrate that this evolution may be accelerated by making people aware of their emerging consensus.
Kinga Makovi, Anahit Sargsyan, Wendi Li, Jean-François Bonnefon et Tahal Rahwan, « Trust within human-machine collectives depends on the perceived consensus about cooperative norms », Nature Communications, n° 3108, mai 2023.
Nature Communications, n° 3108, mai 2023