title: Towards best practices in AGI safety and governance: A survey of expert
    opinion
summary: 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.
paper_authors._id: 6461cc525ea1bc9a9da95171
paper_authors.name: Jonas Schuett
paper_authors.status: claimed_verified
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paper_authors.fullname: Jonas Schuett
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paper_authors.user: jonasschuett
paper_authors._id: 6461cc525ea1bc9a9da95172
paper_authors.name: Noemi Dreksler
paper_authors.status: claimed_verified
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paper_authors.fullname: Noemi Dreksler
paper_authors.type: user
paper_authors.user: noemidreksler
paper_authors._id: 6461cc525ea1bc9a9da95173
paper_authors.name: Markus Anderljung
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paper_authors.fullname: Markus Anderljung
paper_authors.type: user
paper_authors.user: markusanderljung
paper_authors._id: 6461cc525ea1bc9a9da95174
paper_authors.name: David McCaffary
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-16T09:55:22.673Z
paper_authors._id: 62862aca504d3770030879df
paper_authors.avatarUrl: /avatars/d6184246d8a9ba384bd8e7c9760c5efe.svg
paper_authors.fullname: David McCaffary
paper_authors.type: user
paper_authors.user: mccaffary
paper_authors._id: 6461cc525ea1bc9a9da95175
paper_authors.name: Lennart Heim
paper_authors._id: 6461cc525ea1bc9a9da95176
paper_authors.name: Emma Bluemke
paper_authors._id: 6461cc525ea1bc9a9da95177
paper_authors.name: Ben Garfinkel
paper_title: Towards best practices in AGI safety and governance: A survey of expert
    opinion
paper_summary: 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.
paper_ai_summary: Experts agree on a set of risk mitigation practices for AGI labs, including pre-deployment assessments and third-party audits.
paper_ai_keywords: AGI labs pre-deployment risk assessments dangerous capabilities evaluations third-party model audits safety restrictions red teaming