Using artificial agents to nudge outgroup altruism and reduce ingroup favoritism in human-agent interaction
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View abstract on PubMed
Summary
This summary is machine-generated.Promoting outgroup altruism using adaptive artificial agents can weaken ingroup favoritism. A good reputation is key for these agents to improve intergroup cooperation and societal sustainability.
Area Of Science
- Social Psychology
- Human-Computer Interaction
- Artificial Intelligence
Background
- Ingroup favoritism and intergroup discrimination hinder societal cooperation.
- Understanding mechanisms to mitigate these biases is crucial for social sustainability.
Purpose Of The Study
- To investigate if promoting prosocial outgroup altruism can disrupt the ingroup favoritism cycle.
- To explore the role of artificial agents in influencing intergroup dynamics.
Main Methods
- Two studies (N=880) utilized human-agent interaction via a computer-mediated platform.
- Outgroup altruism was introduced using nonadaptive (Study 1) and adaptive (Study 2) artificial agents.
- Machine learning informed adaptive agent behavior; rating tasks controlled for participant awareness.
Main Results
- Nonadaptive agents reinforced ingroup favoritism by reducing cooperation.
- Adaptive agents weakened ingroup favoritism by building a positive reputation with both ingroup and outgroup members.
- Participants perceived adaptive agents as fairer and more human than other humans.
Conclusions
- A positive reputation for outgroup altruism is essential to reduce ingroup favoritism.
- Adaptive artificial agents can effectively promote intergroup cooperation.
- Reputation management is a critical factor in designing effective nudge agents for societal benefit.
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