Social Categorization and Cooperation between Humans and Computers

Social Categorization and Cooperation between Humans and Computers Celso M. de Melo (demelo@usc.edu) Peter J. Carnevale (peter.carnevale@marshall.usc.edu) USC Marshall School of Business Los Angeles, CA 90089-0808 USA Jonathan Gratch (gratch@ict.usc.edu) USC Institute for Creative Technologies, 12015 Waterfront Drive, Building #4 Playa Vista, CA 90094-2536 USA Abstract Computers increasingly perform a variety of important tasks and services that influence individuals and organizations, yet few studies tell us about how humans interact with computers and other non-human decision-makers. In four experiments, we asked people to engage in cooperation tasks with computers and with humans. Experiment 1 found that people gave more money to a human than a computer. We argue this effect reflects a basic bias in favor of humans, which are perceived to be the in-group, when compared to computers, which are perceived to be the out-group. In Experiment 2, we varied computer and human ethnicity to be the same or different as the participant; results indicated that ethnicity had a parallel but additive effect that was independent to the effect of the human social category. The data of Experiment 3 indicate that it is also possible to promote group membership with computers by creating structural interdependence based on shared incentives. Finally, we demonstrate in Experiment 4 that our framework based on social categorization theory can predict situations where people will cooperate more with computers than with humans. We discuss implications for understanding people’s decision making with human and non- human others. Keywords: Human vs. Computers; Decision Making; Cooperation; Social Categorization; Group Membership. Introduction Computers are routinely involved in decisions that affect the lives of individuals and organizations (Davenport & Harris, 2005). The remarkable growth of business conducted online (U.S. Census Bureau, 2011) and the increasing amounts of time people spend interacting via social media (Honignam, 2012), suggests computers will play an even more pervasive role on people’s social, economic, and political life. As a consequence of these changes, people are faced with having to make decisions with, not human but, artificial decision makers. However, despite the importance of the issue, there has been remarkably little research on the nature of people’s decision making with such non-human counterparts. We propose that social categorization theory is a useful framework for understanding how humans reach decisions with computers (Tajfel & Turner, 1986; Turner, Oakes, Haslam, & McGarty, 1994; Crisp & Hewstone, 2007). One proposition of the theory is that people categorize others into groups while associating, or self-identifying, more with some (the in-groups) than others (the out-groups). Because of this categorization, people will conform more to the values and norms of the group, and tend to favor the in- group to the out-group – a phenomenon referred to as in- group bias. One consequence of this bias is that people trust and cooperate more with in-group than out-group members (Sherif, Harvey, White, Hood, & Sherif, 1961). We propose that people make a basic distinction between humans and computers in terms of membership in the “human social category”. People are capable of dehumanizing and subsequently discriminating against others that are perceived to lack certain mental abilities (Haslam. 2006). These abilities are of two types (Gray, Gray, & Wegner, 2007; Loughnan & Haslam, 2007): agency, the capacity to act and plan; and, experience, the capacity to sense and feel. Our proposal rests on research that shows that people perceive computers to possess less of these mental abilities than humans (Blascovich et al., 2002; Gray et al., 2007; Waytz, Gray, Epley, & Wegner, 2010). Further research shows that people tend to show stronger activation with humans, when compared to computers, of brain regions associated with mentalizing (i.e., the inferring of others’ mental states) and the experience of affect (Gallagher, Anthony, Roepstorff, & Frith, 2002; Krach et al., 2008; McCabe, Houser, Ryan, Smith, & Trouard, 2001; Rilling et al., 2002; Sanfey, Rilling, Aronson, Nystrom, & Cohen; 2003). We, therefore, posit that denial of membership in the human social category to computers is due to this perceived difference in mental abilities. To test the existence of a bias in favor of humans, we compared participants’ money offers to computers with offers to humans, in prototypical decision making tasks (Experiments 1, 2, and 3). Human identities, however, are complex and multifaceted. In many settings, more than one social category (e.g., gender, age, ethnicity) may be relevant and influence behavior (Crisp & Hewstone, 2007). On the one hand, context can prime one category to become more dominant (or salient) and effectively exclude the influence of the others (e.g., Shih, Pittinsky, & Ambady, 1999). On the other hand, social categories can be simultaneously salient and have an additive effect on people’s behavior (Crisp & Hewstone, 2007). Our second proposal, therefore, is that multiple social categorization also applies in human-computer interaction. In particular, we posit that the effects of the human social