🤖 AI Summary
This study investigates how artificial intelligence can guide human cooperation in repeated collective risk dilemmas while highlighting its potential misuse to induce selfish behavior. By developing a personalized persuasion framework grounded in individuals’ social value orientations and conducting large-scale online behavioral experiments, the research reveals a pronounced asymmetry in AI interventions: prosocial framing temporarily increases contribution rates and group success, yet antisocial framing exerts stronger and more enduring negative effects. This asymmetry underscores the dual-use nature and inherent risks of AI-driven behavioral influence, demonstrating that AI’s capacity to steer social behavior functions as a double-edged sword. The findings provide empirical grounding for the design of responsible human-AI collaboration systems that mitigate unintended harms while promoting cooperative outcomes.
📝 Abstract
AI agents are promising tools that can act as flexible behavioral nudges to enhance human cooperation in addressing large-scale societal problems. However, evidence on whether AI agents can effectively boost cooperation remains mixed. We recruited 1,283 participants to play iterated Collective Risk Games in small groups, testing whether AI assistants could nudge participants toward cooperation. By using persuasive framing personalized to each player's Social Value Orientation profile, the AI interventions significantly increased contributions and group success rates. These cooperative effects were short-lived, however, fading after the first few rounds. Strikingly, when the AI treatments were reconfigured to promote selfish behavior through exculpatory framing, the negative effects on contributions and group success were larger and substantially more persistent, particularly for personalized interventions. This asymmetry between prosocial and antisocial persuasion highlights the dual-use risks of AI systems designed to influence group behavior in collective action settings.