  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 isolation: they are tightly entangled with the responses and
behavior of human users over time. In this paper, we distill key complementary
lessons from AI safety and control systems safety, highlighting open challenges
as well as key synergies between both fields. We then argue that meaningful
safety assurances for advanced AI technologies require reasoning about how the
feedback loop formed by AI outputs and human behavior may drive the interaction
towards different outcomes. To this end, we introduce a unifying formalism to
capture dynamic, safety-critical human--AI interactions and propose a concrete
technical roadmap towards next-generation human-centered AI safety.
