Cooperation with autonomous machines through culture and emotion

Celso M de Melo and Kazunori Terada

PLOS ONE, 2019, vol. 14, issue 11, 1-12

Abstract: As machines that act autonomously on behalf of others–e.g., robots–become integral to society, it is critical we understand the impact on human decision-making. Here we show that people readily engage in social categorization distinguishing humans (“us”) from machines (“them”), which leads to reduced cooperation with machines. However, we show that a simple cultural cue–the ethnicity of the machine’s virtual face–mitigated this bias for participants from two distinct cultures (Japan and United States). We further show that situational cues of affiliative intent–namely, expressions of emotion–overrode expectations of coalition alliances from social categories: When machines were from a different culture, participants showed the usual bias when competitive emotion was shown (e.g., joy following exploitation); in contrast, participants cooperated just as much with humans as machines that expressed cooperative emotion (e.g., joy following cooperation). These findings reveal a path for increasing cooperation in society through autonomous machines.

Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0224758

DOI: 10.1371/journal.pone.0224758

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