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Safety is Essential for Responsible Open-Ended Systems

Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi, Ruta Binkyte, Mario Fritz

TL;DR

Open-Ended AI promises continuous novelty and adaptive capability but brings substantial safety challenges due to inherent unpredictability and evolving goals. The paper formalizes OE definitions, analyzes risks (unpredictability, misalignment, traceability, resource demands, and social impact), and outlines a suite of mitigations centered on oversight, constraints, adaptive alignment, and safety evaluations. It provides concrete actions for industry, academia, policy makers, and funders to develop risk-aware OE research and governance, including dynamic benchmarks and red-teaming. The work advocates safety-by-design as essential to harness OE AI’s benefits while safeguarding society and the environment.

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.

Safety is Essential for Responsible Open-Ended Systems

TL;DR

Open-Ended AI promises continuous novelty and adaptive capability but brings substantial safety challenges due to inherent unpredictability and evolving goals. The paper formalizes OE definitions, analyzes risks (unpredictability, misalignment, traceability, resource demands, and social impact), and outlines a suite of mitigations centered on oversight, constraints, adaptive alignment, and safety evaluations. It provides concrete actions for industry, academia, policy makers, and funders to develop risk-aware OE research and governance, including dynamic benchmarks and red-teaming. The work advocates safety-by-design as essential to harness OE AI’s benefits while safeguarding society and the environment.

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.

Paper Structure

This paper contains 21 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. What is Open-Endedness
  3. Definition
  4. Applications
  5. Safety of Open-Ended AI
  6. Challenges and Risks
  7. Unpredictability
  8. Creativity vs. Control
  9. Misalignment
  10. Traceability
  11. Resource Constraints
  12. Trade-offs
  13. Social and Human risks
  14. Technical Mitigations of Risks
  15. Oversight
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Open-Ended (OE) AI generates increasingly novel artifacts over time and can be promising to co-evolve with their environments and societal values, hopefully leading to creative solutions, discoveries, and advances for humanity. However, this position paper argues that due to unpredictability, difficulty to control, and cascading misalignment, they can result in catastrophic risks that are harmful and threaten societal and global stability.
  • Figure 2: The Impossible Triangle of OE AI shows that safety, speed of generating artifacts and novelty cannot be satisfied simultaneously; one has to be capped depending on the application.