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Internal decision-making bodies, roles, authority structures, and accountability frameworks that establish who has power over AI-related decisions and how they are held responsible.
Also in Organisation
Reasoning
Establishes formal governance framework and accountability procedures within organization's direct control.
Introduce robust and meaningful accountability mechanisms
especially in evaluating capabilities thresholds, with clear processes that ensure the correct mitigations or courses of action are followed if the thresholds are met. This may include board sign-off for the responsible capability scaling policy, and named individual accountability for key decisions.
2.1 Oversight & AccountabilityEstablish effective risk governance
to ensure that risks are appropriately identified, assessed, and addressed, and their nature and scale transparently reported. Most importantly, provide internal checks and balances, which may include thoughtful separation of roles within risk management.
2.1 Oversight & AccountabilityInclude verification mechanisms
such that external actors can have increased confidence that responsible capability scaling policies are executed as intended. Potential mechanisms for information sharing are included in ‘Model Reporting and Information Sharing’.
3.3.1 Industry CoordinationModel evaluations and red teaming
Frontier AI may pose increased risks of harm related to misuse, loss of control, and other societal risks. Different methods are being developed to assess AI systems and their potential harmful impacts. Model evaluations- such as benchmarking- can be used to produce quantitative, easily replicable measurements of the capabilities and other traits of AI systems. Red teaming provides an alternative approach, which involves observing an AI system from the perspective of an adversary to understand how they could compromise or misuse it. Assessments like model evaluations and red teaming could help to understand the risks frontier AI systems pose and their potential harmful impacts, and help frontier AI organisations, regulators, and users to make informed decisions about training, securing, and deploying them. As methods for assessing frontier AI systems are still emerging, it is important to support and share information about the development and testing of these methods.
2.2.2 Testing & EvaluationResponsible capability scaling
As capability scales, many questions surrounding model development and deployment will warrant significant care. These questions include: what models to develop and how what level of security these models warrant during development whether and how to deploy a model, for instance whether it should be deployed through an API or open-sourced what datasets to use in training what guidance, if any, to provide to users what safeguards to be put in place
2.1.3 Policies & ProceduresModel reporting and information sharing
Transparency around frontier AI can help governments to effectively realise the benefits of AI and mitigate AI risks. Transparency can also encourage sharing of best practices across frontier AI organisations, enable users to make well-informed choices about whether and how to use AI systems, and increase public trust, helping to drive AI adoption. Reporting and sharing information where appropriate could ensure that different parties can access the information they need to support effective governance, develop best practice, inform decision-making about the use of AI systems, and build public trust. Some reporting practices- such as model cards- are already used among frontier AI organisations, whereas other practices included here are areas for future consideration. Given the recent rapid pace of progress in AI, the appropriate government and international governance institutions are still being considered and we recognise that limits the ability of frontier AI organisations to share information with governments, even where it would be desirable. Throughout this section ‘relevant government authorities’ is used to indicate a good practice for information sharing with governments while recognising such relevant authorities may still be under development.
3.3.1 Industry CoordinationModel reporting and information sharing > Share model-agonistic information
3.3.1 Industry CoordinationModel reporting and information sharing > Share model-specific information
Sharing information about specific frontier AI models allows external actors to develop a more granular picture of ongoing AI development and potential risks that will need to be addressed.
3.3.1 Industry CoordinationModel reporting and information sharing > Share different information with different parties
99 OtherSecurity controls including securing model weights
To ensure the safety of frontier AI, consideration of cyber security, protective security risk management and insider risk mitigation is key. Cyber security, both of models and the systems that deploy them, must be considered from the outset of development to ensure that the benefits of AI can be realised. Cyber security is a key underpinning for the safety, reliability, predictability, ethics and potential regulatory compliance of an AI system. To avoid putting safety or sensitive data at risk, it is important to consider the cyber security of AI systems, as well as models in isolation, and to implement cyber security processes throughout the AI lifecycle, particularly where that component is a foundation for other systems. As AI systems advance, developers must maintain an awareness of possible attacks, identify vulnerabilities and implement mitigations. Failure to do so will risk designing vulnerabilities into future AI models and systems. A Secure by Design approach allows developers to ‘bake in’ security from the outset of design and development. Cyber security must be considered in concert with physical and personnel security. Developing a coherent, holistic, risk based and proportionate security strategy, supported by effective governance structures, is essential. Where the compromise of an AI system could lead to tangible or widespread physical damage, significant loss of business operations, leakage of sensitive or confidential information, reputational damage and/or legal challenge, then it is important that AI security risks are treated as mission critical.
2.3.2 Access & Security ControlsSecurity controls including securing model weights > Implement strong cyber security measures and processes (including security evaluations) across their AI systems, including underlying infrastructure and supply chains
2.3 Operations & SecurityEmerging processes for frontier AI safety
UK Department for Science, Innovation and Technology (2023)
The UK recognises the enormous opportunities that AI can unlock across our economy and our society. However, without appropriate guardrails, such technologies can pose significant risks. The AI Safety Summit will focus on how best to manage the risks from frontier AI such as misuse, loss of control and societal harms. Frontier AI organisations play an important role in addressing these risks and promoting the safety of the development and deployment of frontier AI. The UK has therefore encouraged frontier AI organisations to publish details on their frontier AI safety policies ahead of the AI Safety Summit hosted by the UK on 1 to 2 November 2023. This will provide transparency regarding how they are putting into practice voluntary AI safety commitments and enable the sharing of safety practices within the AI ecosystem. Transparency of AI systems can increase public trust, which can be a significant driver of AI adoption. This document complements these publications by providing a potential list of frontier AI organisations’ safety policies. These have been gathered after extensive research and will need updating regularly given the emerging nature of this technology. The safety processes are not listed in order of importance but are summarised in themes. The government is not suggesting or mandating any particular combination of policies – merely detailing the current suite available so that others can understand, interpret and compare frontier companies’ safety policies. This document contains the world’s first overview of emerging safety processes focused on frontier AI and is intended to be a useful tool to boost transparency. This conversation is for frontier AI and whilst it is important that safety is applied throughout the AI sector, it is also important that innovation is not stifled, hence why policies must be proportionate and based on capabilities which are the key driver of risk. This document contains processes and associated practices that some frontier AI organisations are already implementing and others that are being considered within academia and broader civil society. It is intended as a guide for readers of frontier AI companies’ AI safety policies to better understand what good policy might look like, though organisations themselves will be best placed to determine their applicability. Through this exercise, the government intends to help inform dialogue on potential appropriate measures for individual organisations to consider at the UK AI Safety Summit.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure