  A number of leading AI companies, including OpenAI, Google DeepMind, and
Anthropic, have the stated goal of building artificial general intelligence
(AGI) - AI systems that achieve or exceed human performance across a wide range
of cognitive tasks. In pursuing this goal, they may develop and deploy AI
systems that pose particularly significant risks. While they have already taken
some measures to mitigate these risks, best practices have not yet emerged. To
support the identification of best practices, we sent a survey to 92 leading
experts from AGI labs, academia, and civil society and received 51 responses.
Participants were asked how much they agreed with 50 statements about what AGI
labs should do. Our main finding is that participants, on average, agreed with
all of them. Many statements received extremely high levels of agreement. For
example, 98% of respondents somewhat or strongly agreed that AGI labs should
conduct pre-deployment risk assessments, dangerous capabilities evaluations,
third-party model audits, safety restrictions on model usage, and red teaming.
Ultimately, our list of statements may serve as a helpful foundation for
efforts to develop best practices, standards, and regulations for AGI labs.
