Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

Open-ended AI systems face unique safety challenges that differ from traditional AI safety concerns. The paper identifies that as AI systems become more creative and capable of continuous innovation (through open-ended search), controlling their behavior becomes fundamentally harder—creating a tension between allowing systems to be creative and keeping them safe and predictable.

The creativity-control tradeoff is central to responsible open-ended AI development. The paper argues that the more freedom we give AI systems to innovate and explore solutions independently, the harder it becomes to predict and control what they will do, raising questions about whether we can maintain safety guarantees while enabling the open-ended learning that makes these systems powerful.

Insights from artificial life research are critical for AI safety. The paper proposes that decades of work in artificial life and open-ended evolution—fields that have long studied how to create systems that innovate continuously—offer valuable lessons that the broader AI safety community should draw upon when designing safer creative AI systems.

Current AI safety approaches may be insufficient for open-ended systems. Traditional safety methods focus on controlling AI behavior in defined tasks, but open-ended search systems that continuously generate new goals, behaviors, or designs may require fundamentally different safety frameworks that the field has not yet fully developed.

Concrete research questions about open-ended AI safety remain largely unanswered. The paper identifies that we lack clear answers to how to build AI systems that are simultaneously creative, useful, and reliably safe—suggesting this is an urgent area requiring interdisciplinary research.