Safety is Essential for Responsible Open-Ended Systems

2025-02-06

AI Summary

This paper addresses fundamental safety risks—such as alignment loss, behavioral unpredictability, and control failure—arising from the dynamic self-evolution of open-ended AI systems. To this end, it introduces the first systematic safety risk analysis framework for such systems. Methodologically, the work integrates AI alignment theory, complex systems modeling, value-sensitive design, and multi-stakeholder collaborative governance, proposing a dual-track mitigation paradigm: “layered governance” and “dynamic alignment.” Its core contributions are threefold: (1) it internalizes safety as the primary design principle of open-endedness; (2) it establishes a safety-first development roadmap; and (3) it delivers actionable, domain-specific risk assessment tools and governance guidelines for researchers, industry practitioners, and policymakers. Collectively, these advances support the responsible and sustainable evolution of open-ended AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. A growing area of interest within this field is Open-Endedness - the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions. This has become relevant for accelerating scientific discovery and enabling continual adaptation in AI agents. This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks, including challenges in maintaining alignment, predictability, and control. This paper systematically examines these challenges, proposes mitigation strategies, and calls for action for different stakeholders to support the safe, responsible and successful development of Open-Ended AI.

Problem

• Addressing risks in Open-Ended AI systems
• Ensuring alignment and control in AI
• Proposing safety strategies for AI development

Innovation

• Foundation models
• Curiosity-driven learning
• Open-Ended AI safety