Human-Android Interaction in the Near and Distant Future

Social Preferences Toward Humans and Machines: A Systematic Experiment on the Role of Machine Payoffs

There is growing interest in the field of cooperative artificial intelligence (AI), that is, settings in which humans and machines cooperate. By now, more than 160 studies from various disciplines have reported on how people cooperate with machines in behavioral experiments. Our systematic review of the experimental instructions reveals that the implementation of the machine payoffs and the information participants receive about them differ drastically across these studies. In an online experiment (N = 1,198), we compare how these different payoff implementations shape people's revealed social preferences toward machines. When matched with machine partners, people reveal substantially stronger social preferences and reciprocity when they know that a human beneficiary receives the machine payoffs than when they know that no such "human behind the machine" exists. When participants are not informed about machine payoffs, we found weak social preferences toward machines. Comparing survey answers with those from a follow-up study (N = 150), we conclude that people form their beliefs about machine payoffs in a self-serving way. Thus, our results suggest that the extent to which humans cooperate with machines depends on the implementation and information about the machine's earnings.

Do We Take a Robot's Needs into Account? The Effect of Humanization on Prosocial Considerations Toward Other Human Beings and Robots

Robots are becoming an integral part of society, yet the extent to which we are prosocial toward these nonliving objects is unclear. While previous research shows that we tend to take care of robots in high-risk, high-consequence situations, this has not been investigated in more day-to-day, low-consequence situations. Thus, we utilized an experimental paradigm (the Social Mindfulness "SoMi" paradigm) that involved a trade-off between participants' own interests and their willingness to take their task partner's needs into account. In two experiments, we investigated whether participants would take the needs of a robotic task partner into account to the same extent as when the task partner was a human (Study I), and whether this was modulated by participant's anthropomorphic attributions to said robot (Study II). In Study I, participants were presented with a social decision-making task, which they performed once by themselves (solo context) and once with a task partner (either a human or a robot). Subsequently, in Study II, participants performed the same task, but this time with both a human and a robotic task partner. The task partners were introduced via neutral or anthropomorphic priming stories. Results indicate that the effect of humanizing a task partner indeed increases our tendency to take someone else's needs into account in a social decision-making task. However, this effect was only found for a human task partner, not for a robot. Thus, while anthropomorphizing a robot may lead us to save it when it is about to perish, it does not make us more socially considerate of it in day-to-day situations.

Who Sees Human? The Stability and Importance of Individual Differences in Anthropomorphism

Anthropomorphism is a far-reaching phenomenon that incorporates ideas from social psychology, cognitive psychology, developmental psychology, and the neurosciences. Although commonly considered to be a relatively universal phenomenon with only limited importance in modern industrialized societies-more cute than critical-our research suggests precisely the opposite. In particular, we provide a measure of stable individual differences in anthropomorphism that predicts three important consequences for everyday life. This research demonstrates that individual differences in anthropomorphism predict the degree of moral care and concern afforded to an agent, the amount of responsibility and trust placed on an agent, and the extent to which an agent serves as a source of social influence on the self. These consequences have implications for disciplines outside of psychology including human-computer interaction, business (marketing and finance), and law. Concluding discussion addresses how understanding anthropomorphism not only informs the burgeoning study of nonpersons, but how it informs classic issues underlying person perception as well.