Cooperation Through Indirect Reciprocity in Child-Robot Interactions

TL;DR

This paper explores how indirect reciprocity can promote cooperation in child-robot interactions, combining experiments and modeling to understand trust, prosociality, and learning dynamics in human-AI groups.

Contribution

It demonstrates that indirect reciprocity extends to children and robots, and shows how children's strategies influence AI learning of cooperation.

Findings

IR extends to children and robots in coordination dilemmas

Children's strategies guide AI algorithms to learn cooperation

Cooperation depends on human strategies revealed in interactions

Abstract

Social interactions increasingly involve artificial agents, such as conversational or collaborative bots. Understanding trust and prosociality in these settings is fundamental to improve human-AI teamwork. Research in biology and social sciences has identified mechanisms to sustain cooperation among humans. Indirect reciprocity (IR) is one of them. With IR, helping someone can enhance an individual's reputation, nudging others to reciprocate in the future. Transposing IR to human-AI interactions is however challenging, as differences in human demographics, moral judgements, and agents' learning dynamics can affect how interactions are assessed. To study IR in human-AI groups, we combine laboratory experiments and theoretical modelling. We investigate whether 1) indirect reciprocity can be transposed to children-robot interactions; 2) artificial agents can learn to cooperate given children's strategies; and 3) how differences in learning algorithms impact human-AI cooperation. We find that IR extends to children and robots solving coordination dilemmas. Furthermore, we observe that the strategies revealed by children provide a sufficient signal for multi-armed bandit algorithms to learn cooperative actions. Beyond the experimental scenarios, we observe that cooperating through multi-armed bandit algorithms is highly dependent on the strategies revealed by humans.