Social decisions and fairness change when people’s interests are represented by autonomous agents

There has been growing interest on agents that represent people’s interests or act on their behalf such as automated negotiators, self-driving cars, or drones. Even though people will interact often with others via these agent representatives, little is known about whether people’s behavior changes when acting through these agents, when compared to direct interaction with others. Here we show that people’s decisions will change in important ways because of these agents; specifically, we showed that interacting via agents is likely to lead people to behave more fairly, when compared to direct interaction with others. We argue this occurs because programming an agent leads people to adopt a broader perspective, consider the other side’s position, and rely on social norms—such as fairness—to guide their decision making. To support this argument, we present four experiments: in Experiment 1 we show that people made fairer offers in the ultimatum and impunity games when interacting via agent representatives, when compared to direct interaction; in Experiment 2, participants were less likely to accept unfair offers in these games when agent representatives were involved; in Experiment 3, we show that the act of thinking about the decisions ahead of time—i.e., under the so-called “strategy method”—can also lead to increased fairness, even when no agents are involved; and, finally, in Experiment 4 we show that participants were less likely to reach an agreement with unfair counterparts in a negotiation setting. We discuss theoretical implications for our understanding of the nature of people’s social behavior with agent representatives, as well as practical implications for the design of agents that have the potential to increase fairness in society

Human cooperation when acting through autonomous machines

Recent times have seen an emergence of intelligent machines that act autonomously on our behalf, such as autonomous vehicles. Despite promises of increased efficiency, it is not clear whether this paradigm shift will change how we decide when our self-interest (e.g., comfort) is pitted against the collective interest (e.g., environment). Here we show that acting through machines changes the way people solve these social dilemmas and we present experimental evidence showing that participants program their autonomous vehicles to act more cooperatively than if they were driving themselves. We show that this happens because programming causes selfish short-term rewards to become less salient, leading to considerations of broader societal goals. We also show that the programmed behavior is influenced by past experience. Finally, we report evidence that the effect generalizes beyond the domain of autonomous vehicles. We discuss implications for designing autonomous machines that contribute to a more cooperative society