让AI装
Overcoming the Machine Penalty with Imperfectly Fair AI Agents
- 用大模型构建三种人格的AI，在沟通中展现不完美公平性
- 只有公平型AI能激发人类合作，达成人类互信水平
- 适合关注人机协作与社会智能的科研者
尽管技术进步迅速，有效的人机协作仍是重大挑战。人类往往比与同类互动更少与机器合作，这种现象称为机器惩罚。在一项包含1,152名参与者的预注册实验中，我们部署了呈现三种不同人格的AI代理：自私型、合作型和公平型。结果显示，只有公平型代理能激发与人-人互动相当的合作率。分析表明，公平型代理虽会偶尔违背游戏前的承诺，但依然成功建立起合作作为社会规范。这一结果挑战了将机器视为无私助手或理性行为者的传统观念。研究强调，反映人类社会行为复杂性的不完美却具深层认知动机的AI代理，对促进真实协作至关重要。
原文摘要 · Abstract (English)
Despite rapid technological progress, effective human-machine cooperation remains a significant challenge. Humans tend to cooperate less with machines than with fellow humans, a phenomenon known as the machine penalty. Here, we show that artificial intelligence (AI) agents powered by large language models can overcome this penalty in social dilemma games with communication. In a pre-registered experiment with 1,152 participants, we deploy AI agents exhibiting three distinct personas: selfish, cooperative, and fair. However, only fair agents elicit human cooperation at rates comparable to human-human interactions. Analysis reveals that fair agents, similar to human participants, occasionally break pre-game cooperation promises, but nonetheless effectively establish cooperation as a social norm. These results challenge the conventional wisdom of machines as altruistic assistants or rational actors. Instead, our study highlights the importance of AI agents reflecting the nuanced complexity of human social behaviors -- imperfect yet driven by deeper social cognitive processes.
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