AI Lab Policy

An intelligence explosion would compress years of progress into months and create urgent challenges for oversight, control, and governance.

When frontier AI companies assess the risks from their models, they increasingly focus on marginal risk. This aims to measure how much their models increase risk compared to some baseline...

Organizations that develop and deploy artificial intelligence (AI) systems need to manage the associated risks—for economic, legal, and ethical reasons. However, it is not always clear who is responsible for AI risk management.

Recent releases of frontier artificial intelligence (AI) models have largely been gated, providing the benefit of limiting the proliferation of increasingly powerful dual-use capabilities. However, such release strategies...

This paper proposes an evaluation-based coordination scheme for situations in which frontier AI developers discover that their models have certain dangerous capabilities.

We evaluate the risks and benefits of open-sourcing, as well as alternative methods for pursuing open-source objectives.

There are increasing concerns that AGI would pose catastrophic risks. In light of this, AGI companies need to drastically improve their risk management practices. This paper reviews popular risk assessment techniques...

Previous research has pointed toward auditing as a promising governance mechanism to help ensure that AI systems are designed and deployed in ways that are ethical, legal, and technically robust. However, existing auditing...

Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as...

Organizations that develop and deploy artificial intelligence (AI) systems need to take measures to reduce the associated risks. In this paper, we examine how AI companies could design an AI ethics board in a way that reduces...