This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize AI's ability to mimic human intelligence, and instead advocate for a symbiotic relationship where distinct intelligences enhance each other. We analyze key elements of human-horse relationships: trust, communication, and mutual adaptability, to highlight essential principles for human-AI collaboration. Trust is critical in both partnerships, built through predictability and shared understanding, while communication and feedback loops foster mutual adaptability. We further discuss the importance of taming and habituation in shaping these interactions, likening it to how humans train AI to perform reliably and ethically in real-world settings. The article also addresses the asymmetry of responsibility, where humans ultimately bear the greater burden of oversight and ethical judgment. Finally, we emphasize that long-term commitment and continuous learning are vital in both human-horse and human-AI relationships, as ongoing interaction refines the partnership and increases mutual adaptability. By drawing on these insights from human-horse interactions, we offer a vision for designing AI systems that are trustworthy, adaptable, and capable of fostering symbiotic human-AI partnerships.

翻译：本文探讨了人类与马匹的互动关系，并将其作为理解和设计有效人机协作关系的隐喻。借鉴人类与马匹悠久的协作历史，我们提出人工智能（AI）应像马匹一样，作为人类能力的补充而非替代。我们超越了传统基准（如图灵测试所强调的AI模仿人类智能的能力），转而倡导一种不同智能体相互增强的共生关系。通过分析人马关系中的关键要素——信任、沟通与相互适应性，我们揭示了人机协作的核心原则。信任在两种协作关系中均至关重要，它通过可预测性和共同理解得以建立；而沟通与反馈循环则促进相互适应。我们进一步探讨了驯化与习惯化在塑造这些互动中的重要性，并将其类比为人类训练AI在现实场景中可靠且合乎伦理地运作的过程。本文还讨论了责任不对称问题，即人类最终需承担更重的监督与伦理判断责任。最后，我们强调长期承诺与持续学习在人马关系及人机关系中都至关重要，因为持续的互动能优化协作关系并提升相互适应性。通过借鉴人马互动的这些洞见，我们为设计可信赖、适应性强且能促进共生型人机协作的AI系统提供了新的视角。