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Safety cases, assurance plans, and documented evidence of safety claims.
Also in Risk & Assurance
AGI labs should publish the results or summaries of internal risk assessments, unless this would unduly reveal proprietary information or itself produce significant risk. This should include a justification of why the lab is willing to accept remaining risks.*
Reasoning
Publishing risk assessment results with risk acceptance justification creates documented evidence supporting organizational safety claims.
Alignment Techniques
AGI labs should implement state-of-the-art safety and alignment techniques.
1.1 ModelAPI access to powerful models
AGI labs should strongly consider only deploying powerful models via an application programming interface (API).
2.3.1 Deployment ManagementAvoid Capabilities Jumps
AGI labs should not deploy models that are much more capable than any existing models.*
2.3.1 Deployment ManagementAvoiding Hype
AGI labs should avoid releasing powerful models in a way that is likely to create hype around AGI (e.g. by overstating results or announcing them in attention-grabbing ways).
2.1.3 Policies & ProceduresBackground checks
AGI labs should perform rigorous background checks before hiring/appointing members of the board of directors, senior executives, and key employees.*
2.3.2 Access & Security ControlsBoard risk committee
AGI labs should have a board risk committee, i.e. a permanent committee within the board of directors which oversees the lab’s risk management practices.*
2.1.1 Leadership OversightTowards best practices in AGI safety and governance: A survey of expert opinion
Schuett, Jonas; Dreksler, Noemi; Anderljung, Markus; McCaffary, David; Heim, Lennart; Bluemke, Emma; Garfinkel, Ben (2023)
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.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
