Generative artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the potential for individual and societal harm. Today, the predominant paradigm for human-AI safety focuses on fine-tuning the generative model's outputs to better agree with human-provided examples or feedback. In reality, however, the consequences of an AI model's outputs cannot be determined in an isolated context: they are tightly entangled with the responses and behavior of human users over time. In this position paper, we argue that meaningful safety assurances for these AI technologies can only be achieved by reasoning about how the feedback loop formed by the AI's outputs and human behavior may drive the interaction towards different outcomes. To this end, we envision a high-value window of opportunity to bridge the rapidly growing capabilities of generative AI and the dynamical safety frameworks from control theory, laying a new foundation for human-centered AI safety in the coming decades.

翻译：生成式人工智能（AI）正以前所未有的规模与人类互动，为带来巨大积极影响提供了新途径，但也引发了关于个人和社会潜在危害的广泛担忧。当前，人机安全的主流范式集中于通过微调生成模型的输出，使其更好地匹配人类提供的示例或反馈。然而，在现实中，AI模型输出的后果无法在孤立情境中确定：它们与人类用户随时间变化的反应和行为紧密交织。在本立场论文中，我们认为，要为这些AI技术提供有意义的安全保障，必须推断由AI输出和人类行为形成的反馈回路如何将互动导向不同结果。为此，我们展望了一个高价值的机遇窗口：将生成式AI快速发展的能力与控制理论的动态安全框架相连接，为未来几十年以人为中心的AI安全奠定新基础。