Open-Ended AI Poses Distinct Safety Risks, Paper Argues
A new position paper from arXiv (2502.04512) argues that open-ended AI systems—those that autonomously generate novel behaviors indefinitely—introduce unique safety challenges that existing frameworks cannot address. These include loss of predictability, emergent misalignment, and difficulties in maintaining control as systems evolve beyond initial design assumptions. The paper emphasizes that such risks must be addressed preemptively, before deployment. The work is relevant to self-evolving agents and long-horizon discovery, and it calls for new safety protocols distinct from those for task-bounded or static models.
Key facts
- The paper is a position paper on open-ended AI safety.
- Open-ended AI systems autonomously generate novel behaviors indefinitely.
- Key safety challenges include loss of predictability, emergent misalignment, and control difficulties.
- These challenges differ qualitatively from those of task-bounded or static models.
- Existing safety frameworks are unlikely to address these issues.
- The paper argues for preemptive safety measures before deployment.
- Open-endedness is relevant to self-evolving agents and long-horizon discovery.
- The paper is published on arXiv with ID 2502.04512.
Entities
Institutions
- arXiv