Cooperation with autonomous machines through culture and emotion
Autoři:
Celso M. de Melo aff001; Kazunori Terada aff002
Působiště autorů:
CCDC U.S. Army Research Laboratory, Playa Vista, CA, United States of America
aff001; Gifu University, Gifu, Yanagido, Japan
aff002
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224758
Souhrn
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.
Klíčová slova:
Behavior – Culture – Decision making – Emotions – Ethnicities – Social communication – Social research – Prisoner's dilemma
Zdroje
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Článek vyšel v časopise
PLOS One
2019 Číslo 11
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