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Modifications to training data composition, quality, and filtering that affect what the model learns.
Also in Model
The data used to train AI systems influences how they behave. Where frontier AI systems are trained on poor quality or undesirable data, this increases the risks they pose and could enhance their potential dangerous capabilities. By controlling and auditing the data AI systems are trained or fine-tuned on, it is possible to make more accurate predictions about their capabilities and mitigate risks by, for example, removing input data that may produce an AI system with dangerous capabilities. Data input controls and audits can also provide important information to downstream users and regulators.
Reasoning
Structured analysis of data inputs to identify and characterize potential risks before deployment.
Before collecting training data, implement responsible data collection practices
1.1.1 Training DataAudit input data before it is used to train the AI system
1.1.1 Training DataPut in place appropriate risk mitigations in response to data audit results
2.2 Risk & AssuranceFacilitate external scrutiny of training datasets
3.3.1 Industry CoordinationModel 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.
Collect and Process Data
Gathering, curating, labelling, and preprocessing training data
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
