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Frontier AI Regulation: Managing Emerging Risks to Public Safety
To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models.
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities.
Why and How Governments Should Monitor AI Development
In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems.
Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society
One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research.
The Transformative Potential of Artificial Intelligence
We suggest that the term 'transformative AI' is a helpful alternative, reflecting the possibility that advanced AI systems could have very large impacts on society without reaching human-level cognitive abilities.
The tension between openness and prudence in AI research
This paper explores the tension between openness and prudence in AI research, evident in two core principles of the Montr\'eal Declaration for Responsible AI.
Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning
The aim of this paper is to facilitate nuanced discussion around research norms and practices to mitigate the harmful impacts of advances in machine learning (ML).
