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Observation and professional dialogue assessment matrix

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Observation and professional dialogue assessment matrix

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LST - Session 2 - Classroom management 17.10.2024

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Link to current IfATE page

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Institute for Apprenticeships and Technical Education

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Details of standard

Occupation summary

This occupation is found in all parts of the Further Education and Skills Sector (FES), usually in settings where students are aged 16 and above (a small number of teachers may teach 14-16 year olds in alternative provision, including FE colleges). The majority of Further Education, Learning and Skills Teachers will work in FE colleges (whether general or specialist), independent training providers (ITPs), Adult Community Learning (ACL) providers or offender learning. A small number may work as trainers in large organisations. Teaching can be delivered at a range of levels including essential skills generally up to Level 3 including, academic, vocational and technical education, some teachers may deliver subjects up to post-graduate level; it will also cover a broad range of both academic and technical subject/sector areas. Many Learning and Skills Teachers are dual professionals drawing on their technical and vocational as well as academic experience and qualifications to provide a high-quality learning experience.

The broad purpose of the occupation is to ensure that students achieve the best possible knowledge, skills and behaviours. Learning and Skills Teachers do this by planning and delivering teaching that is current, comprehensive and challenging, and that inspires students to engage, progress and achieve their full potential.

In their daily work, an employee in this occupation interacts with other Teachers and FES professionals and students of different ages, abilities, backgrounds and ambitions. They may also interact with employers and stakeholders. Teachers who are specialists in a particular technical or vocational subject, sector or occupation will typically maintain close working relationships with others working in their industry/sector and keep their own knowledge and skills up to date.

An employee in this occupation is responsible for helping to prepare each of their students for progression to/or within employment or to further study. To deliver this to a diverse group of students, including those with special educational needs, a Learning and Skills Teacher needs to be flexible, innovative, and adaptable, changing their behaviour and teaching style to suit the environment and level of their students. A Learning and Skills Teacher needs to have a sound understanding of pedagogy and regularly engage with robust evidence of effective teaching and learning methods and practice relevant to their subject area(s). A Learning and Skills Teacher typically has significant autonomy and needs to demonstrate consistently high standards of personal and professional conduct and is accountable for their professional development. A Learning and Skills Teacher may be responsible for the managing of others, as well as managing budgets.

Typical job titles include:

Occupation duties

Qualifications

English and Maths

Apprentices without level 2 English and maths will need to achieve this level prior to taking the End-Point Assessment. For those with an education, health and care plan or a legacy statement, the apprenticeship's English and maths minimum requirement is Entry Level 3. A British Sign Language (BSL) qualification is an alternative to the English qualification for those whose primary language is BSL.

Additional details

Occupational Level:

5

Duration (months):

18

Review

this apprenticeship will be reviewed in accordance with our change request policy.

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This occupation is found in all parts of the Further Education and Skills Sector (FES), usually in settings where students are aged 16 and above (a small number of teachers may teach 14-16 year olds in alternative provision, including FE colleges).

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Link to 16 personalities quiz:

Free Personality Test | 16Personalities

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360° feedback questionnaire

Complete one 'self-reflection'

Gain at least 3 more completed versions -

1 from superior, 1 from same level, 1 from subordinate level

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ONLY applicable if completing L5 Diploma rather than L5 LST Apprenticeship

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How to Build a More Student-Led Classroom (Because We're Doing Waaayy Too Much for Our Students! )

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https://tinyurl.com/53ewnbna

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LST Session 6 - 13.03.2025 Using AI in FE

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Full AlphaGo documentary

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AlphaGo - The Movie | Full award-winning documentary

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Short video: What is Artificial Intelligience? 

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Short video: What is Artificial Intelligience? 

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What Is AI? | Artificial Intelligence | What is Artificial Intelligence? | AI In 5 Mins |Simplilearn

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Artificial intelligence (AI) has made enormous strides in recent years and has increasingly moved into the public consciousness.

Explore Trendscape

Our take on the interconnected global trends that are shaping the business climate for our clients.

Increases in computational power, coupled with advances in machine learning, have fueled the rapid rise of AI. This has brought enormous opportunities, as new AI applications have given rise to new ways of doing business. It has also brought potential risks, from unintended impacts on individuals (e.g., AI errors harming an individual's credit score or public reputation) to the risk of misuse of AI by malicious third parties (e.g., by manipulating AI systems to produce inaccurate or misleading output, or by using AI to create deepfakes).

Governments and regulatory bodies around the world have had to act quickly to try to ensure that their regulatory frameworks do not become obsolete. In addition, international organizations such as the G7, the UN, the Council of Europe and the OECD have responded to this technological shift by issuing their own AI frameworks. But they are all scrambling to stay abreast of technological developments, and already there are signs that emerging efforts to regulate AI will struggle to keep pace. In an effort to introduce some degree of international consensus, the UK government organized the first global AI Safety Summit in November 2023, with the aim of encouraging the safe and responsible development of AI around the world.

Most jurisdictions have sought to strike a balance between encouraging AI innovation and investment, while at the same time attempting to create rules to protect against possible harms. However, jurisdictions around the world have taken substantially different approaches to achieving these goals, which has in turn increased the risk that businesses face from a fragmented and inconsistent AI regulatory environment. Nevertheless, certain trends are becoming clearer at this stage:

1. "AI" means different things in different jurisdictions: One of the foundational challenges that any international business faces when designing an AI regulatory compliance strategy is figuring out what constitutes "AI." Unfortunately, the definition of AI varies from one jurisdiction to the next. For example, the EU AI Act adopts a definition of "AI systems" that is based on (but is not identical to) the OECD's definition, and which leaves room for substantial doubt due to its uncertain wording. Canada has proposed a similar, though more concise, definition. Various US states have proposed their own definitions, which differ from one another. And many jurisdictions (e.g., the UK, Israel, China, and Japan) do not currently provide a comprehensive definition of AI. Because several of the proposed AI regulations have extraterritorial effect (meaning more than one AI regulation may apply simultaneously), international businesses may be forced to adopt a "highest common denominator" approach to identifying AI based on the strictest applicable standard.

2. Emerging AI regulations come in different forms: The various emerging AI regulations have no consistent legal form - some are statutes, some are executive orders, some are expansions of existing regulatory frameworks, and so on. The EU AI Act is a "Regulation" (which means that most of it will apply directly in all EU Member States, without the need for national implementation in most cases). The UK has taken a different approach, declining to legislate at this early stage in the development of AI, and instead choosing to task existing UK regulators with the responsibility of interpreting and applying five AI principles in their respective spheres. In the US, there is a mix of White House Executive Orders, federal and state initiatives, and actions by existing regulatory agencies, such as the Federal Trade Commission. As a result, the types of compliance obligations that international businesses face are likely to be materially different from one jurisdiction to the next. Many other jurisdictions have yet to decide whether they will issue sector-specific or generally applicable rules and have yet to decide between creating new regulators or expanding the roles of existing regulators, making it challenging for businesses to anticipate what form their AI regulatory relationships will take in the long term.

3. Emerging AI regulations have different conceptual approaches: The next difficulty is the lack of a consistent conceptual approach among emerging AI regulations around the world - some are legally binding while others are not, some are sector-specific while others apply across all sectors, some will be enforced by regulators while others are merely guidelines or recommendations, and so on. As noted above, the UK approach is to use existing regulators to implement five AI principles, but with no new explicit legal obligations. This has the advantage of meaning that businesses will deal with AI regulators with whom they are already familiar but has the disadvantage that different UK regulators may interpret these principles differently in their respective spheres. The EU AI Act is cross-sectoral and creates new regulatory and enforcement powers for existing bodies, including the European Commission, and also creates entirely new bodies such as the AI Board and the AI Office, while leaving EU Member States to appoint their own AI regulators tasked with enforcing the EU AI Act. In the US, the Federal Trade Commission, Equal Employment Opportunity Commission, Consumer Financial Protection Bureau, and Department of Justice issued a joint statement clarifying that their existing authority covers AI, while various state regulators are also likely to have competence to regulate AI. International organizations including the OECD, the UN, and the G7 have issued AI principles, but these impose no legal obligations on businesses. In principle, these initiatives encourage consistency across members of each organization, but in practice this does not seem to have worked.

4. Flexibility is a double-edged sword: In an effort to create AI regulations that can adapt to technological advances that have not yet been anticipated, many jurisdictions have sought to include substantial flexibility in those regulations, either by using deliberately high-level wording and policies, or by allowing for future interpretation and application by courts and regulators. This has the obvious advantage of prolonging the lifespan of such regulations by allowing them to be adapted to future technologies. However, it also creates the disadvantage of uncertainty because it leaves businesses uncertain of how their compliance obligations will be interpreted in the future. This is likely to mean that it is harder for businesses to know whether their planned implementations of AI will be lawful in the medium-to-long term and may make it harder to attract long-term AI investment in those jurisdictions.

5. The overlap between AI regulation and other areas of law is complex: A substantial number of laws that are not directly focused on AI nevertheless apply to AI by association within their respective spheres, meaning that any use of AI will often trigger compliance issues and legal challenges even where there is not (yet) any enforceable AI-specific law. These areas of overlap include: IP (e.g., IP infringement issues with respect to AI model training data, and questions about copyright and patentability of AI-assisted inventions); antitrust; data protection (which adds restrictions to processing of personal data, and in some cases imposes special compliance obligations for processing carried out by automated means, including by AI); M&A (where AI innovation is driving dealmaking in many markets); financial regulation (where financial regulatory requirements may limit the ways in which AI can lawfully be deployed); litigation; digital infrastructure; securities; global trade; foreign direct investment; mining & metals; and so on. This overlap will mean that many businesses need to understand not just AI regulations in general, but also any rules that affect the use of AI in the context of the relevant sector or business activity.

Businesses in almost all sectors need to keep a close eye on these developments to ensure that they are aware of the AI regulations and forthcoming trends, in order to identify new opportunities and new potential business risks. But even at this early stage, the inconsistent approaches each jurisdiction has taken to the core questions of how to regulate AI is clear. As a result, it appears that international businesses may face substantially different AI regulatory compliance challenges in different parts of the world. To that end, this AI Tracker is designed to provide businesses with an understanding of the state of play of AI regulations in the core markets in which they operate. It provides analysis of the approach that each jurisdiction has taken to AI regulation and provides helpful commentary on the likely direction of travel.

Because global AI regulations remain in a constant state of flux, this AI Tracker will develop over time, adding updates and new jurisdictions when appropriate. Stay tuned, as we continue to provide insights to help businesses navigate these ever-evolving issues.

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AI Watch: Global regulatory tracker | White & Case LLP

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Keeping track of AI regulatory developments around the world.

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Presented to Parliament by the Secretary of State for Science, Innovation and Technology by Command of His Majesty on 29 March 2023.

Command Paper Number: 815

© Crown copyright 2023

ISBN: 978-1-5286-4009-1

Ministerial foreword

The Rt Hon Michelle Donelan MP, Secretary of State for Science, Innovation and Technology

I believe that a common-sense, outcomes-oriented approach is the best way to get right to the heart of delivering on the priorities of people across the UK. Better public services, high quality jobs and opportunities to learn the skills that will power our future - these are the priorities that will drive our goal to become a science and technology superpower by 2030.

Artificial Intelligence ( AI) will play a central part in delivering and enabling these goals, and this white paper will ensure we are putting the UK on course to be the best place in the world to build, test and use AI technology. But we are not starting from zero. Having invested over £2.5 billion in AI since 2014, this paper builds on our recent announcements of £110 million for our AI Tech Missions Fund, £900 million to establish a new AI Research Resource and to develop an exascale supercomputer capable of running large AI models - backed up by our new £8 million AI Global Talent Network and £117 million of existing funding to create hundreds of new PhDs for AI researchers.

Most of us are only now beginning to understand the transformative potential of AI as the technology rapidly improves. But in many ways, AI is already delivering fantastic social and economic benefits for real people - from improving NHS medical care to making transport safer. Recent advances in things like generative AI give us a glimpse into the enormous opportunities that await us in the near future if we are prepared to lead the world in the AI sector with our values of transparency, accountability and innovation.

These risks could include anything from physical harm, an undermining of national security, as well as risks to mental health. The development and deployment of AI can also present ethical challenges which do not always have clear answers. Unless we act, household consumers, public services and businesses will not trust the technology and will be nervous about adopting it. Unless we build public trust, we will miss out on many of the benefits on offer.

That is why our white paper details how we intend to support innovation while providing a framework to ensure risks are identified and addressed. However, a heavy-handed and rigid approach can stifle innovation and slow AI adoption. That is why we set out a proportionate and pro-innovation regulatory framework. Rather than target specific technologies, it focuses on the context in which AI is deployed. This enables us to take a balanced approach to weighing up the benefits versus the potential risks.

We recognise that particular AI technologies, foundation models for example, can be applied in many different ways and this means the risks can vary hugely. For example, using a chatbot to produce a summary of a long article presents very different risks to using the same technology to provide medical advice. We understand the need to monitor these developments in partnership with innovators while also avoiding placing unnecessary regulatory burdens on those deploying AI.

To ensure our regulatory framework is effective, we will leverage the expertise of our world class regulators. They understand the risks in their sectors and are best placed to take a proportionate approach to regulating AI. This will mean supporting innovation and working closely with business, but also stepping in to address risks when necessary. By underpinning the framework with a set of principles, we will drive consistency across regulators while also providing them with the flexibility needed.

For innovators working at the cutting edge and developing novel technologies, navigating regulatory regimes can be challenging. That's why we are confirming our commitment to taking forward a key recommendation made by Sir Patrick Vallance to establish a regulatory sandbox for AI. This will bring together regulators to support innovators directly and help them get their products to market. The sandbox will also enable us to understand how regulation interacts with new technologies and refine this interaction where necessary.

Our approach relies on collaboration between government, regulators and business. Initially, we do not intend to introduce new legislation. By rushing to legislate too early, we would risk placing undue burdens on businesses. But alongside empowering regulators to take a lead, we are also setting expectations. Our new monitoring functions will provide a real time assessment of how the regulatory framework is performing so that we can be confident that it is proportionate. The pace of technological development also means that we need to understand new and emerging risks, engaging with experts to ensure we take action where necessary. A critical component of this activity will be engaging with the public to understand their expectations, raising awareness of the potential of AI and demonstrating that we are responding to concerns.

The framework set out in this white paper is deliberately designed to be flexible. As the technology evolves, our regulatory approach may also need to adjust. Our principles-based approach, with central functions to monitor and drive collaboration, will enable us to adapt as needed while providing industry with the clarity needed to innovate. We will continue to develop our approach, building on our commitment to making the UK the best place in the world to be a business developing and using AI. Responses to the consultation will inform how we develop the regulatory framework - I encourage all of those with an interest to respond.

Executive summary

Artificial intelligence - the opportunity and the challenge

1. Artificial intelligence ( AI) is already delivering wide societal benefits, from medical advances to mitigating climate change. For example, an AI technology developed by DeepMind, a UK-based business, can now predict the structure of almost every protein known to science. This breakthrough will accelerate scientific research and the development of life-saving medicines - it has already helped scientists to make huge progress in combating malaria, antibiotic resistance, and plastic waste.

2. The UK Science and Technology Framework sets out government's strategic vision and identifies AI as one of 5 critical technologies. The framework notes the role of regulation in creating the environment for AI to flourish. We know that we have yet to see AI technologies reach their full potential. Under the right conditions, AI will transform all areas of life and stimulate the UK economy by unleashing innovation and driving productivity, creating new jobs and improving the workplace.

4. While we should capitalise on the benefits of these technologies, we should also not overlook the new risks that may arise from their use, nor the unease that the complexity of AI technologies can produce in the wider public. We already know that some uses of AI could damage our physical and mental health, infringe on the privacy of individuals[footnote 10] and undermine human rights.[footnote 11]

5. Public trust in AI will be undermined unless these risks, and wider concerns about the potential for bias and discrimination, are addressed. By building trust, we can accelerate the adoption of AI across the UK to maximise the economic and social benefits that the technology can deliver, while attracting investment and stimulating the creation of high-skilled AI jobs.[footnote 12] In order to maintain the UK's position as a global AI leader, we need to ensure that the public continues to see how the benefits of AI can outweigh the risks.[footnote 13]

Our pro-innovation framework

9. We are taking a deliberately agile and iterative approach, recognising the speed at which these technologies are evolving. Our framework is designed to build the evidence base so that we can learn from experience and continuously adapt to develop the best possible regulatory regime. Industry has praised our pragmatic and proportionate approach.

10. Our framework is underpinned by 5 principles to guide and inform the responsible development and use of AI in all sectors of the economy:

Safety, security and robustness

Appropriate transparency and explainability

Fairness

Accountability and governance

Contestability and redress

11. We will not put these principles on a statutory footing initially. New rigid and onerous legislative requirements on businesses could hold back AI innovation and reduce our ability to respond quickly and in a proportionate way to future technological advances. Instead, the principles will be issued on a non-statutory basis and implemented by existing regulators. This approach makes use of regulators' domain-specific expertise to tailor the implementation of the principles to the specific context in which AI is used. During the initial period of implementation, we will continue to collaborate with regulators to identify any barriers to the proportionate application of the principles, and evaluate whether the non-statutory framework is having the desired effect.

12. Following this initial period of implementation, and when parliamentary time allows, we anticipate introducing a statutory duty on regulators requiring them to have due regard to the principles. Some feedback from regulators, industry and academia suggested we should implement further measures to support the enforcement of the framework. A duty requiring regulators to have regard to the principles should allow regulators the flexibility to exercise judgement when applying the principles in particular contexts, while also strengthening their mandate to implement them. In line with our proposal to work collaboratively with regulators and take an adaptable approach, we will not move to introduce such a statutory duty if our monitoring of the framework shows that implementation is effective without the need to legislate.

13. In the 2022 AI regulation policy paper,[footnote 15] we proposed a small coordination layer within the regulatory architecture. Industry and civil society were supportive of our intention to ensure coherence across the AI regulatory framework. However, feedback often argued strongly for greater central coordination to support regulators on issues requiring cross-cutting collaboration and ensure that the overall regulatory framework functions as intended.

14. We have identified a number of central support functions required to make sure that the overall framework offers a proportionate but effective response to risk while promoting innovation across the regulatory landscape:

Monitoring and evaluation of the overall regulatory framework's effectiveness and the implementation of the principles, including the extent to which implementation supports innovation. This will allow us to remain responsive and adapt the framework if necessary, including where it needs to be adapted to remain effective in the context of developments in AI's capabilities and the state of the art.

Assessing and monitoring risks across the economy arising from AI.

Conducting horizon scanning and gap analysis, including by convening industry, to inform a coherent response to emerging AI technology trends.

Supporting testbeds and sandbox initiatives to help AI innovators get new technologies to market.

Providing education and awareness to give clarity to businesses and empower citizens to make their voices heard as part of the ongoing iteration of the framework.

Promoting interoperability with international regulatory frameworks.

15. The central support functions will initially be provided from within government but will leverage existing activities and expertise from across the broader economy. The activities described above will neither replace nor duplicate the work undertaken by regulators and will not involve the creation of a new AI regulator.

16. Our proportionate approach recognises that regulation is not always the most effective way to support responsible innovation. The proposed framework is aligned with, and supplemented by, a variety of tools for trustworthy AI, such as assurance techniques, voluntary guidance and technical standards. Government will promote the use of such tools. We are collaborating with partners like the UK AI Standards Hub to ensure that our overall governance framework encourages responsible AI innovation (see part 4 for details).

17. In keeping with the global nature of these technologies, we will also continue to work with international partners to deliver interoperable measures that incentivise the responsible design, development and application of AI. During our call for views, industry, academia and civil society stressed that international alignment should support UK businesses to capitalise on global markets and protect UK citizens from cross-border harms.

18. The UK is frequently ranked third in the world across a range of measures, including level of investment, innovation and implementation of AI.[footnote 16] To make the UK the most attractive place in the world for AI innovation and support UK companies wishing to export and attract international investment, we must ensure international compatibility between approaches. Countries around the world, as well as multilateral forums, are exploring approaches to regulating AI. Thanks to our reputation for pragmatic regulation, the UK is rightly seen by international partners as a leader in this global conversation.

Part 1: Introduction

1.1 The power and potential of artificial intelligence

19. AI is already delivering major advances and efficiencies in many areas. AI quietly automates aspects of our everyday activities, from systems that monitor traffic to make our commutes smoother,[footnote 17] to those that detect fraud in our bank accounts.[footnote 18] AI has revolutionised large-scale safety-critical practices in industry, like controlling the process of nuclear fusion.[footnote 19] And it has also been used to accelerate scientific advancements, such as the discovery of new medicine[footnote 20] or the technologies we need to tackle climate change.[footnote 21]

20. But this is just the beginning. AI can be used in a huge variety of settings and has the extraordinary potential to transform our society and economy.[footnote 22] It could have as much impact as electricity or the internet, and has been identified as one of the 5 critical technologies in the UK Science and Technology Framework.[footnote 23] As AI becomes more powerful, and as innovators explore new ways to use it, we will see more applications of AI emerge. As a result, AI has a huge potential to drive growth[footnote 24] and create jobs.[footnote 25] It will support people to carry out their existing jobs, by helping to improve workforce efficiency and workplace safety.[footnote 26] To remain world leaders in AI, attract global talent and create high-skilled jobs in the UK, we must create a regulatory environment where such innovation can thrive.

21. Technological advances like large language models ( LLMs) are an indication of the transformative developments yet to come.[footnote 27] LLMs provide substantial opportunities to transform the economy and society. For example, LLMs can automate the process of writing code and fixing programming bugs. The technology can support genetic medicine by identifying links between genetic sequences and medical conditions. It can support people to review and summarise key points from lengthy documents. In the last 4 years, LLMs have been developed beyond expectations and they are becoming applicable to an increasingly wide range of tasks.[footnote 28] We expand on the development of LLM and other foundation models in section 3.3.3 below.

Box 1.1: Examples of AI opportunities

AI helps piece together the first complete image of a black hole

AI can enable scientific discovery. A computer vision model was used to piece together the first ever image of a black hole 55 million light years away, combining images from 8 telescopes around the world.[footnote 29]
AI solves decades old protein-folding puzzle
An AI company based in the UK trained neural networks to predict the structures of proteins, solving a problem that had long stumped scientists. The predictions are advancing the field of structural biology: scientists have already used them to prevent antibiotic resistance,[footnote 30] advance disease research,[footnote 31] and accelerate the fight against plastic pollution.[footnote 32] As we find more uses for AI, it will rewrite scientific fields and change the way we learn about our world.
Deep learning AI could improve breast cancer screening
AI could transform how diseases are detected, prevented, and treated. Doctors are testing if deep learning can be applied to breast cancer screening. Currently, every mammogram is double-checked by radiologists but this is labour-intensive and causes diagnosis delays. A UK medical technology company is working with the NHS to test AI for the second screening, meaning greater numbers of patients could be screened faster and clinicians could spend more time with patients and provide faster access to treatment.[footnote 33]
Farming efficiency increased by AI robots
Applying robotics and AI to field management can make farming more efficient, sustainable and productive. Lightweight, autonomous mapping and monitoring robots operating across the UK can spend hours on the field in all conditions and significantly reduce soil compaction. These systems can digitise the field, providing farmers with data to improve weed and pest management. If these systems become widely used, they could contribute to agricultural and horticultural productivity, reduce the pressure of labour shortages and better preserve the environment.[footnote 34]
AI helps accelerate the discovery of new medicines
Significant time and resources are currently needed to develop new and effective medicines. AI can accelerate the discovery of new medicines by quickly identifying potential biologically active compounds from millions of candidates within a short period.[footnote 35] Scientists may also have succeeded in using generative AI to design antibodies that bind to a human protein linked to cancer.[footnote 36]
AI is used in the fight against the most serious and harmful crimes
The Child Images Abuse Database[footnote 37] uses the powerful data processing capabilities of AI to identify victims and perpetrators of child sexual abuse. The quick and effective identification of victims and perpetrators in digital abuse images allows for real world action to remove victims from harm and ensure their abusers are held to account. The use of AI increases the scale and speed of analysis while protecting staff welfare by reducing their exposure to distressing content.
AI increases cyber security capabilities
Companies providing cyber security services are increasingly using AI to analyse large amounts of data about malware and respond to vulnerabilities in network security at faster-than-human speeds.[footnote 38] As the complexity of the cyber threat landscape evolves, the pattern-recognition and recursive learning capabilities of AI are likely to play an increasingly significant role in proactive cyber defence against malicious actors.

22. The concept of AI is not new, but recent advances in data generation and processing have changed the field and the technology it produces. For example, while recent developments in the capabilities of generative AI models have created exciting opportunities, they have also sparked new debates about potential AI risks.[footnote 39] As AI research and development continues at pace and scale, we expect to see even greater impact and public awareness of AI risks.[footnote 40]

24. We have made an initial assessment of AI-specific risks and their potential to cause harm, with reference in our analysis to the values that they threaten if left unaddressed. These values include safety, security, fairness, privacy and agency, human rights, societal well-being and prosperity.

25. Our assessment of cross-cutting AI risk identified a range of high-level risks that our framework will seek to prioritise and mitigate with proportionate interventions. For example, safety risks include physical damage to humans and property, as well as damage to mental health.[footnote 42] AI creates a range of new security risks to individuals, organisations, and critical infrastructure.[footnote 43] Without government action, AI could cause and amplify discrimination that results in, for example, unfairness in the justice system.[footnote 44] Similarly, without regulatory oversight, AI technologies could pose risks to our privacy and human dignity, potentially harming our fundamental liberties.[footnote 45] Our regulatory intervention will ensure that AI does not cause harm at a societal level, threatening democracy[footnote 46] or UK values.

Box 1.2: Illustrative AI risks

The patchwork of legal frameworks that currently regulate some uses of AI may not sufficiently address the risks that AI can pose. The following examples are hypothetical scenarios designed to illustrate AI's potential to create harm.
Risks to human rights
Generative AI is used to generate deepfake pornographic video content, potentially damaging the reputation, relationships and dignity of the subject.
Risks to safety
An AI assistant based on LLM technology recommends a dangerous activity that it has found on the internet, without understanding or communicating the context of the website where the activity was described. The user undertakes this activity causing physical harm.

An AI tool assessing credit-worthiness of loan applicants is trained on incomplete or biased data, leading the company to offer loans to individuals on different terms based on characteristics like race or gender.
Risks to privacy and agency
Connected devices in the home may constantly gather data, including conversations, potentially creating a near-complete portrait of an individual's home life. Privacy risks are compounded the more parties can access this data.
Risks to societal wellbeing
Disinformation generated and propagated by AI could undermine access to reliable information and trust in democratic institutions and processes.
Risks to security
AI tools can be used to automate, accelerate and magnify the impact of highly targeted cyber attacks, increasing the severity of the threat from malicious actors. The emergence of LLMs enables hackers[footnote 48] with little technical knowledge or skill to generate phishing campaigns with malware delivery capabilities.[footnote 49]

1.3 A note on terminology

Terminology used in this paper:[footnote 50]

or or AI technologies: products and services that are 'adaptable' and 'autonomous' in the sense outlined in our definition in section 3.2.1.

AI supplier: any organisation or individual who plays a role in the research, development, training, implementation, deployment, maintenance, provision or sale of AI systems.

: any individual or organisation that uses an AI product.

AI life cycle: all events and processes that relate to an AI system's lifespan, from inception to decommissioning, including its design, research, training, development, deployment, integration, operation, maintenance, sale, use and governance.

AI ecosystem: the complex network of actors and processes that enable the use and supply of AI throughout the AI life cycle (including supply chains, markets, and governance mechanisms).

Foundation model: a type of AI model that is trained on a vast quantity of data and is adaptable for use on a wide range of tasks. Foundation models can be used as a base for building more specific AI models. Foundation models are discussed in more detail in section 3.3.3 below.[footnote 51]

Impacted third party: an individual or company that is impacted by the outcomes of the AI systems that they do not use or supply themselves.

Part 2: The current regulatory environment

26. The UK's AI success is, in part, due to our reputation for high-quality regulators and our strong approach to the rule of law, supported by our technology-neutral legislation and regulations. UK laws, regulators and courts already address some of the emerging risks posed by AI technologies (see box 2.1 for examples). This strong legal foundation encourages investment in new technologies, enabling AI innovation to thrive,[footnote 52] and high-quality jobs to flourish.[footnote 53]

Box 2.1: Example of legal coverage of AI in the UK and potential gaps

Discriminatory outcomes that result from the use of AI may contravene the protections set out in the Equality Act 2010.[footnote 54] AI systems are also required by data protection law to process personal data fairly.[footnote 55] However, AI can increase the risk of unfair bias or discrimination across a range of indicators or characteristics. This could undermine public trust in AI.

Product safety laws ensure that goods manufactured and placed on the market in the UK are safe. Product-specific legislation (such as for electrical and electronic equipment,[footnote 56] medical devices,[footnote 57] and toys[footnote 58]) may apply to some products that include integrated AI. However, safety risks specific to AI technologies should be monitored closely. As the capability and adoption of AI increases, it may pose new and substantial risks that are unaddressed by existing rules.

Consumer rights law[footnote 59] may protect consumers where they have entered into a sales contract for AI-based products and services. Certain contract terms (for example, that goods are of satisfactory quality, fit for a particular purpose, and as described) are relevant to consumer contracts. Similarly, businesses are prohibited from including certain terms in consumer contracts. Tort law provides a complementary regime that may provide redress where a civil wrong has caused harm. It is not yet clear whether consumer rights law will provide the right level of protection in the context of products that include integrated AI or services based on AI, or how tort law may apply to fill any gap in consumer rights law protection.

27. While AI is currently regulated through existing legal frameworks like financial services regulation,[footnote 60] some AI risks arise across, or in the gaps between, existing regulatory remits. Industry told us that conflicting or uncoordinated requirements from regulators create unnecessary burdens and that regulatory gaps may leave risks unmitigated, harming public trust and slowing AI adoption.

28. Industry has warned us that regulatory incoherence could stifle innovation and competition by causing a disproportionate amount of smaller businesses to leave the market. If regulators are not proportionate and aligned in their regulation of AI, businesses may have to spend excessive time and money complying with complex rules instead of creating new technologies. Small businesses and start-ups often do not have the resources to do both.[footnote 61] With the vast majority of digital technology businesses employing under 50 people,[footnote 62] it is important to ensure that regulatory burdens do not fall disproportionately on smaller companies, which play an essential role in the AI innovation ecosystem and act as engines for economic growth and job creation.[footnote 63]

29. Regulatory coordination will support businesses to invest confidently in AI innovation and build public trust by ensuring real risks are effectively addressed. While some regulators already work together to ensure regulatory coherence for AI through formal networks like the AI and digital regulations service in the health sector[footnote 64] and the Digital Regulation Cooperation Forum ( DRCF), other regulators have limited capacity and access to AI expertise. This creates the risk of inconsistent enforcement across regulators. There is also a risk that some regulators could begin to dominate and interpret the scope of their remit or role more broadly than may have been intended in order to fill perceived gaps in a way that increases incoherence and uncertainty. Industry asked us to support further system-wide coordination to clarify who is responsible for addressing cross-cutting AI risks and avoid duplicate requirements across multiple regulators.

Case study 2.1: Addressing AI fairness under the existing legal and regulatory framework

A fictional company, 'AI Fairness Insurance Limited', is designing a new AI-driven algorithm to set prices for insurance premiums that accurately reflect a client's risk. Setting fair prices and building consumer trust is a key component of AI Fairness Insurance Limited's brand so ensuring it complies with the relevant legislation and guidance is a priority.

Fairness in AI systems is covered by a variety of regulatory requirements and best practice. AI Fairness Insurance Limited's use of AI to set prices for insurance premiums could be subject to a range of legal frameworks, including data protection, equality, and general consumer protection laws. It could also be subject to sectoral rules like the Financial Services and Markets Act 2000.[footnote 65]

It can be challenging for a company like AI Fairness Insurance Limited to identify which rules are relevant and confidently apply them to AI use cases. There is currently a lack of support for businesses like AI Fairness Insurance Limited to navigate the regulatory landscape, with no cross-cutting principles and limited system-wide coordination.

30. Government intervention is needed to improve the regulatory landscape. We intend to leverage and build on existing regimes, maximising the benefits of what we already have, while intervening in a proportionate way to address regulatory uncertainty and gaps. This will deliver a pro-innovation regulatory framework that is designed to be adaptable and future-proof, supported by tools for trustworthy AI including assurance techniques and technical standards. This approach will provide more clarity and encourage collaboration between government, regulators and industry to unlock innovation.

Case study 2.2: Adapting regulatory approaches to AI - AI as a medical device

Some UK regulators have led the way and proactively adapted their approaches to AI-enabled technologies.

In 2022, the MHRA (Medicines and Healthcare products Regulatory Agency) published a roadmap clarifying in guidance the requirements for AI and software used in medical devices.[footnote 66] regulator is also updating the regulatory framework for medical devices to protect patients and secure the UK's global reputation for responsible innovation in medical device software.

As part of this work, the MHRA will develop guidance on the transparency and interpretability of AI as a medical device.[footnote 67] The MHRA will consider the specific challenges posed by AI in this context, drawing on the applicable AI regulation cross-sectoral principles and ethical principles for AI in health and social care to issue practical guidance on how to meet legal product safety requirements. The MHRA will work with other regulators such as the Information Commissioner's Office (ICO) and the National Data Guardian to consider patients' data protection and trust in medical devices.

This work will provide manufacturers with clear requirements and guidance to attract responsible innovation to the UK.

Part 3: An innovative and iterative approach

3.1 Aims of the regulatory framework

31. Regulation can increase innovation by giving businesses the incentive to solve important problems while addressing the risk of harm to citizens. For example, product safety legislation has increased innovation towards safer products and services.[footnote 68] In the case of AI, a context-based, proportionate approach to regulation will help strengthen public trust and increase AI adoption.[footnote 69]

32. The National AI Strategy set out our aim to regulate AI effectively and support innovation.[footnote 70] In line with the principles set out in the Plan for Digital Regulation,[footnote 71] our approach to AI regulation will be proportionate; balancing real risks against the opportunities and benefits that AI can generate. We will maintain an effective balance as we implement the framework by focusing on the context and outcomes of AI.

33. Our policy paper proposed a pro-innovation framework designed to give consumers the confidence to use AI products and services, and provide businesses the clarity they need to invest in AI and innovate responsibly.[footnote 72] This approach was broadly welcomed - particularly by industry. Based on feedback, we have distilled our aims into 3 objectives that our framework is designed to achieve:

Drive growth and prosperity by making responsible innovation easier and reducing regulatory uncertainty. This will encourage investment in AI and support its adoption throughout the economy, creating jobs and helping us to do them more efficiently.

To achieve this objective we must act quickly to remove existing barriers to innovation and prevent the emergence of new ones. This will allow AI companies to capitalise on early development successes and achieve long term market advantage.[footnote 73] By acting now, we can give UK innovators a headstart in the global race to convert the potential of AI into long term advantages for the UK, maximising the economic and social value of these technologies and strengthening our current position as a world leader in AI.[footnote 74]

Increase public trust in AI by addressing risks and protecting our fundamental values.

Trust is a critical driver for AI adoption.[footnote 75] If people do not trust AI, they will be reluctant to use it. Such reluctance can reduce demand for AI products and hinder innovation. Therefore we must demonstrate that our regulatory framework (described in section 3.2) effectively addresses AI risks.

Strengthen the UK's position as a global leader in AI. The development of AI technologies can address some of the most pressing global challenges, from climate change to future pandemics. There is also growing international recognition that AI requires new regulatory responses to guide responsible innovation.

The UK can play a central role in the global conversation by shaping international governance and regulation to maximise opportunities and build trust in the technology, while mitigating potential cross-border risks and protecting our democratic values. There is also an important leadership role for the UK in the development of the global AI assurance industry,[footnote 76] including in auditing and safety.

We will ensure that the UK remains attractive to innovators and investors by promoting interoperability with other regulatory approaches and minimising cross-border frictions. We will work closely with global partners through multilateral and bilateral engagements to learn from, influence and adapt as international and domestic approaches to AI regulation continue to emerge (see part 6).

35. Government is taking wider action to ensure the UK retains its status as a global leader in AI, for example by taking forward Sir Patrick Vallance's recommendation relating to intellectual property law and generative AI.[footnote 77] This will ensure we keep the right balance between protecting rights holders and our thriving creative industries, while supporting AI developers to access the data they need.

3.2 The proposed regulatory framework

37. We have identified the essential characteristics of our regulatory regime. Our framework will be pro-innovation, proportionate, trustworthy, adaptable, clear and collaborative.[footnote 79]

Pro-innovation: enabling rather than stifling responsible innovation.

Proportionate: avoiding unnecessary or disproportionate burdens for businesses and regulators.

Trustworthy: addressing real risks and fostering public trust in AI in order to promote and encourage its uptake.

Adaptable: enabling us to adapt quickly and effectively to keep pace with emergent opportunities and risks as AI technologies evolve.

Clear: making it easy for actors in the AI life cycle, including businesses using AI, to know what the rules are, who they apply to, who enforces them, and how to comply with them.

Collaborative: encouraging government, regulators, and industry to work together to facilitate AI innovation, build trust and ensure that the voice of the public is heard and considered.

38. The framework, built around the 4 key elements below, is designed to empower our existing regulators and promote coherence across the regulatory landscape. The 4 key elements are:

Defining AI based on its unique characteristics to support regulator coordination (section 3.2.1).

Adopting a context-specific approach (section 3.2.2).

Providing a set of cross-sectoral principles to guide regulator responses to AI risks and opportunities(section 3.2.3).

The principles clarify government's expectations for responsible AI and describe good governance at all stages of the AI life cycle.

The application of the principles will initially be at the discretion of the regulators, allowing prioritisation according to the needs of their sectors.

Following this initial non-statutory period of implementation, and when parliamentary time allows, we anticipate introducing a statutory duty requiring regulators to have due regard to the principles.

Delivering new central functions to support regulators to deliver the AI regulatory framework, maximising the benefits of an iterative approach and ensuring that the framework is coherent (section 3.2.4).

3.2.1 Defining Artificial Intelligence

39. To regulate AI effectively, and to support the clarity of our proposed framework, we need a common understanding of what is meant by 'artificial intelligence'. There is no general definition of AI that enjoys widespread consensus.[footnote 80] That is why we have defined AI by reference to the 2 characteristics that generate the need for a bespoke regulatory response.

The 'adaptivity' of AI can make it difficult to explain the intent or logic of the system's outcomes:

AI systems are 'trained' - once or continually - and operate by inferring patterns and connections in data which are often not easily discernible to humans.

Through such training, AI systems often develop the ability to perform new forms of inference not directly envisioned by their human programmers.

The 'autonomy' of AI can make it difficult to assign responsibility for outcomes:

Some AI systems can make decisions without the express intent or ongoing control of a human.

40. The combination of adaptivity and autonomy can make it difficult to explain, predict, or control the outputs of an AI system, or the underlying logic by which they are generated. It can also be challenging to allocate responsibility for the system's operation and outputs. For regulatory purposes, this has potentially serious implications, particularly when decisions are made relating to significant matters, like an individual's health, or where there is an expectation that a decision should be justifiable in easily understood terms, like a legal ruling.

41. By defining AI with reference to these functional capabilities and designing our approach to address the challenges created by these characteristics, we future-proof our framework against unanticipated new technologies that are autonomous and adaptive. Because we are not creating blanket new rules for specific technologies or applications of AI, like facial recognition or LLMs, we do not need to use rigid legal definitions. Our use of these defining characteristics was widely supported in responses to our policy paper,[footnote 81] as rigid definitions can quickly become outdated and restrictive with the rapid evolution of AI.[footnote 82] We will, however, retain the ability to adapt our approach to defining AI if necessary, alongside the ongoing monitoring and iteration of the wider regulatory framework.

42. Below, we provide some illustrative examples of AI systems to demonstrate their autonomous and adaptive characteristics. While many aspects of the technologies described in these case studies will be covered by existing law, they illustrate how AI-specific characteristics introduce novel risks and regulatory implications.

Figure 1: Illustration of our strategy for regulating AI

Case study 3.1: Natural language processing in customer service chatbots

Adaptivity: Provides responses to real-time customer messages, having been trained on huge datasets to identify statistical patterns in ordinary human speech, potentially increasing personalisation over time as the system learns from each new experience.

Autonomy: Generates a human-like output based on the customer's text input, to answer queries, help customers find products and services, or send targeted updates. Operates with little need for human oversight or intervention.

Illustrative AI-related regulatory implication: Unintentional inclusion of inaccurate or misleading information in training data, producing harmful instructions or convincingly spreading misinformation.

Case study 3.2: Automated healthcare triage systems

Adaptivity: Predicts patient conditions based on the pathology, treatment and risk factors associated with health conditions from the analysis of medical datasets, patient records and real-time health data.

Autonomy: Generates information about the likely causes of a patient's symptoms and recommends potential interventions and treatments, either to a medical professional or straight to a patient.

Illustrative AI-related regulatory implication: Unclear liability for an AI triage system that provides incorrect medical advice, leading to negative health outcomes for a patient and affecting the patient's ability to obtain redress.

Case study 3.3: Text-to-image generators

Adaptivity: Uses large amounts of online content to learn how to create rich, highly specific images on the basis of a short text prompt.

Autonomy: Based on text input, these systems generate images that mimic the qualities of human-created art, with no ongoing oversight from the user.

Illustrative AI-related regulatory implication: Reproduction of biases or stereotyping in training data, leading to offensive language or content.

43. Industry, regulators, and civil society responded positively to our proposed definition, recognising that it supports our context-based and flexible approach to AI regulation. We will monitor how regulators interpret and apply adaptivity and autonomy when formulating domain-specific definitions of AI. Government will support coordination between regulators when we see potential for better alignment between their interpretations and use of our defining characteristics.

44. Active and collaborative horizon scanning will ensure that we can identify developments and emerging trends, and adapt our framework accordingly. We will convene industry, academia and other key stakeholders to inform economy-wide horizon scanning activity. This work will build on the activity of individual regulators.

3.2.2 Regulating the use - not the technology

45. Our framework is context-specific.[footnote 83] We will not assign rules or risk levels to entire sectors or technologies. Instead, we will regulate based on the outcomes AI is likely to generate in particular applications. For example, it would not be proportionate or effective to classify all applications of AI in critical infrastructure as high risk. Some uses of AI in critical infrastructure, like the identification of superficial scratches on machinery, can be relatively low risk. Similarly, an AI-powered chatbot used to triage customer service requests for an online clothing retailer should not be regulated in the same way as a similar application used as part of a medical diagnostic process.

46. A context-specific approach allows regulators to weigh the risks of using AI against the costs of missing opportunities to do so.[footnote 84] Regulators told us that AI risk assessments should include the failure to exploit AI capabilities. For example, there can be a significant opportunity cost related to not having access to AI in safety-critical operations, from heavy industry,[footnote 85] to personal healthcare (see box 1.1). Sensitivity to context will allow the framework to respond to the level of risk in a proportionate manner and avoid stifling innovation or missing opportunities to capitalise on the social benefits made available by AI.

47. To best achieve this context-specificity we will empower existing UK regulators to apply the cross-cutting principles. Regulators are best placed to conduct detailed risk analysis and enforcement activities within their areas of expertise. Creating a new AI-specific, cross-sector regulator would introduce complexity and confusion, undermining and likely conflicting with the work of our existing expert regulators.

3.2.3 A principles-based approach

48. Existing regulators will be expected to implement the framework underpinned by 5 values-focused cross-sectoral principles:

Safety, security and robustness

Appropriate transparency and explainability

Accountability and governance

Contestability and redress

49. The principles set out the key elements of responsible AI design, development and use, and will help guide businesses. Regulators will lead the implementation of the framework, for example by issuing guidance on best practice for adherence to these principles.

50. Regulators will be expected to apply the principles proportionately to address the risks posed by AI within their remits, in accordance with existing laws and regulations. In this way, the principles will complement existing regulation, increase clarity, and reduce friction for businesses operating across regulatory remits.

51. A principles-based approach allows the framework to be agile and proportionate. It is in line with the Plan for Digital Regulation,[footnote 86] the findings from the independent Taskforce on Innovation, Growth and Regulatory Reform,[footnote 87] the Regulatory Horizons Council's Closing the Gap report on implementing innovation-friendly regulation,[footnote 88] and Sir Patrick Vallance's Regulation for Innovation report.[footnote 89]

52. Since publishing the AI regulation policy paper,[footnote 90] we have updated and strengthened the principles. We have:

Reflected stakeholder feedback by expanding on concepts such as robustness and governance. We have also considered the results of public engagement research that highlighted an expectation for principles such as transparency, fairness and accountability to be included within an AI governance framework.[footnote 91]

Merged the safety principle with security and robustness, given the significant overlap between these concepts.

Better reflected concepts of accountability and responsibility.

Refined each principle's definition and rationale.

Principle: Safety, security, robustness
Definition and explanation
AI systems should function in a robust, secure and safe way throughout the AI life cycle, and risks should be continually identified, assessed and managed.

Regulators may need to introduce measures for regulated entities to ensure that AI systems are technically secure and function reliably as intended throughout their entire life cycle.
Rationale for the principle
The breadth of possible uses for AI and its capacity to autonomously develop new capabilities and functions mean that AI can have a significant impact on safety and security. Safety-related risks are more apparent in certain domains, such as health or critical infrastructure, but they can materialise in many areas. Safety will be a core consideration for some regulators and more marginal for others. However, it will be important for all regulators to assess the likelihood that AI could pose a risk to safety in their sector or domain, and take a proportionate approach to managing it.

Additionally, AI systems should be technically secure and should reliably function as intended and described. System developers should be aware of the specific security threats that could apply at different stages of the AI life cycle and embed resilience to these threats into their systems. Other actors should remain vigilant of security issues when they interact with an AI system. We anticipate that regulators may wish to consider the National Cyber Security Centre (NCSC) principles for securing machine learning models when assessing whether AI actors are adequately prioritising security.[footnote 92]

When applying this principle, regulators will need to consider providing guidance in a way that is coordinated and coherent with the activities of other regulators. Regulators' implementation of this principle may require the corresponding AI life cycle actors to regularly test or carry out due diligence on the functioning, resilience and security of a system.[footnote 93] Regulators may also need to consider technical standards addressing safety, robustness and security to benchmark the safe and robust performance of AI systems and to provide AI life cycle actors with guidance for implementing this principle in their remit.

Principle: Appropriate transparency and explainability
Definition and explanation
AI systems should be appropriately transparent and explainable.

Transparency refers to the communication of appropriate information about an AI system to relevant people (for example, information on how, when, and for which purposes an AI system is being used). Explainability refers to the extent to which it is possible for relevant parties to access, interpret and understand the decision-making processes of an AI system.[footnote 94]

An appropriate level of transparency and explainability will mean that regulators have sufficient information about AI systems and their associated inputs and outputs to give meaningful effect to the other principles (for example, to identify accountability). An appropriate degree of transparency and explainability should be proportionate to the risk(s) presented by an AI system.

Regulators may need to look for ways to support and encourage relevant life cycle actors to implement appropriate transparency measures, for example through regulatory guidance. Parties directly affected by the use of an AI system should also be able to access sufficient information about AI systems to be able to enforce their rights. In applying the principle to their business processes, relevant life cycle actors may be asked to provide this information in the form and manner required by regulators, including through product labelling. Technical standards could also provide useful guidance on available methods to assess, design, and improve transparency and explainability within AI systems - recognising that consumers, users and regulators will require different information.[footnote 95]
Rationale for the principle
Transparency can increase public trust,[footnote 96] which has been shown to be a significant driver of AI adoption.[footnote 97]

When AI systems are not sufficiently explainable, AI suppliers and users risk inadvertently breaking laws, infringing rights, causing harm and compromising the security of AI systems.

At a technical level, the explainability of AI systems remains an important research and development challenge. The logic and decision-making in AI systems cannot always be meaningfully explained in a way that is intelligible to humans, although in many settings this poses no substantial risk. It is also true that in some cases, a decision made by AI may perform no worse on explainability than a comparable decision made by a human.[footnote 98] Future developments of the technology may pose additional challenges to achieving explainability. AI systems should display levels of explainability that are appropriate to their context, including the level of risk and consideration of what is achievable given the state of the art.

Principle: Fairness
Definition and explanation
AI systems should not undermine the legal rights of individuals or organisations, discriminate unfairly against individuals or create unfair market outcomes. Actors involved in all stages of the AI life cycle should consider definitions of fairness that are appropriate to a system's use, outcomes and the application of relevant law.

Fairness is a concept embedded across many areas of law and regulation, including equality and human rights, data protection, consumer and competition law, public and common law, and rules protecting vulnerable people.

Regulators may need to develop and publish descriptions and illustrations of fairness that apply to AI systems within their regulatory domain, and develop guidance that takes into account relevant law, regulation, technical standards,[footnote 99] and assurance techniques.

Regulators will need to ensure that AI systems in their domain are designed, deployed and used considering such descriptions of fairness. Where concepts of fairness are relevant in a broad range of intersecting regulatory domains, we anticipate that developing joint guidance will be a priority for regulators.
Rationale for the principle
In certain circumstances, AI can have a significant impact on people's lives, including insurance offers, credit scores, and recruitment outcomes. AI-enabled decisions with high impact outcomes should not be arbitrary and should be justifiable.

In order to ensure a proportionate and context-specific approach regulators should be able to describe and illustrate what fairness means within their sectors and domains, and consult with other regulators where multiple remits are engaged by a specific use case. We expect that regulators' interpretations of fairness will include consideration of compliance with relevant law and regulation, including:

1. AI systems should not produce discriminatory outcomes, such as those which contravene the Equality Act 2010 or the Human Rights Act 1998. Use of AI by public authorities should comply with the additional duties placed on them by legislation (such as the Public Sector Equality Duty).

2. Processing of personal data involved in the design, training, and use of AI systems should be compliant with requirements under the UK General Data Protection Regulation (GDPR), the Data Protection Act 2018,[footnote 100] particularly around fair processing and solely automated decision-making.

3. Consumer and competition law, including rules protecting vulnerable consumers and individuals.[footnote 101]

4. Relevant sector-specific fairness requirements, such as the Financial Conduct Authority (FCA) Handbook.

Principle: Accountability and governance
Definition and explanation
Governance measures should be in place to ensure effective oversight of the supply and use of AI systems, with clear lines of accountability established across the AI life cycle.

AI life cycle actors should take steps to consider, incorporate and adhere to the principles and introduce measures necessary for the effective implementation of the principles at all stages of the AI life cycle.

Regulators will need to look for ways to ensure that clear expectations for regulatory compliance and good practice are placed on appropriate actors in the AI supply chain, and may need to encourage the use of governance procedures that reliably ensure these expectations are met.

Regulator guidance on this principle should reflect that 'accountability' refers to the expectation that organisations or individuals will adopt appropriate measures to ensure the proper functioning, throughout their life cycle, of the AI systems that they research, design, develop, train, operate, deploy, or otherwise use.
Rationale for the principle
AI systems can operate with a high level of autonomy, making decisions about how to achieve a certain goal or outcome in a way that has not been explicitly programmed or foreseen.[footnote 102] Establishing clear, appropriate lines of ownership and accountability is essential for creating business certainty while ensuring regulatory compliance.

Doing so for actors in the AI life cycle is difficult, given the complexity of AI supply chains, as well as the adaptivity, autonomy and opacity of AI systems. In some cases, technical standards can provide useful guidance on good practices for AI governance.[footnote 103] Assurance techniques like impact assessments can help to identify potential risks early in the development life cycle, enabling their mitigation through appropriate safeguards and governance mechanisms.

Regulatory guidance should also reflect the responsibilities such life cycle actors have for demonstrating proper accountability and governance (for example, by providing documentation on key decisions throughout the AI system life cycle, conducting impact assessments or allowing audits where appropriate).

Principle: Contestability and redress
Definition and explanation
Where appropriate, users, impacted third parties and actors in the AI life cycle should be able to contest an AI decision or outcome that is harmful or creates material risk of harm.

Regulators will be expected to clarify existing routes to contestability and redress, and implement proportionate measures to ensure that the outcomes of AI use are contestable where appropriate.

We would also expect regulators to encourage and guide regulated entities to make clear routes (including informal channels) easily available and accessible, so affected parties can contest harmful AI outcomes or decisions as needed.
Rationale for the principle
The use of AI technologies can result in different types of harm and can have a material impact on people's lives. AI systems' outcomes may introduce risks such as the reproduction of biases or safety concerns.

People and organisations should be able to contest outcomes where existing rights have been violated or they have been harmed.

It will be important for regulators to provide clear guidance on this principle so that AI life cycle actors can implement it in practice. This should include clarifying that appropriate transparency and explainability are relevant to good implementation of this contestability and redress principle.

The UK's initial non-statutory approach will not create new rights or new routes to redress at this stage.

53. We anticipate that regulators will need to issue guidance on the principles or update existing guidance to provide clarity to business. Regulators may also publish joint guidance on one or more of the principles, focused on AI use cases that cross multiple regulatory remits. We are keen to work with regulators and industry to understand the best approach to providing guidance. We expect that practical guidance will support actors in the AI life cycle to adhere to the principles and embed them into their technical and operational business processes. Regulators may also use alternative measures and introduce other tools or resources, in addition to issuing guidance, within their existing remits and powers to implement the principles.

54. Government will monitor the overall effectiveness of the principles and the wider impact of the framework.[footnote 104] This will include working with regulators to understand how the principles are being applied and whether the framework is adequately supporting innovation.
Consultation questions:
1. Do you agree that requiring organisations to make it clear when they are using AI would improve transparency?

2. Are there other measures we could require of organisations to improve AI transparency?

3. Do you agree that current routes to contest or get redress for AI-related harms are adequate?

4. How could current routes to contest or seek redress for AI-related harms be improved, if at all?

5. Do you agree that, when implemented effectively, the revised cross-sectoral principles will cover the risks posed by AI technologies?

6. What, if anything, is missing from the revised principles?

Case study 3.4: Explainable AI in practice

The level of explainability needed from an AI system is highly specific to its context, including the extent to which an application is safety-critical. The level and type of explainability required will likely vary depending on whether the intended audience of the explanation is a regulator, technical expert, or lay person.

For example, a technical expert designing self-driving vehicles would need to understand the system's decision-making capabilities to test, assess and refine them. In the same context, a lay person may need to understand the decision-making process only in order to use the vehicle safely. If the vehicle malfunctioned and caused a harmful outcome,[footnote 105] a regulator may need information about how the system operates in order to allocate responsibility - similar to the level of explainability currently needed to hold human drivers accountable.

While AI explainability remains a technical challenge and an area of active research, regulators are already conducting work to address it. In 2021, the ICO and the Alan Turing Institute issued co-developed guidance on explaining decisions made with AI,[footnote 106] giving organisations practical advice to help explain the processes, services and decisions delivered or assisted by AI to the individuals affected by them.

The audience for an explanation of AI's outcomes will often be a regulator, who may require a higher standard of explainability depending on the risks represented by an application. The MHRA's Project Glass Box work is addressing the challenge of setting medical device requirements that take into account adequate consideration of human interpretability and its consequences for the safety and effectiveness for AI used in medical devices.[footnote 107]

Case study 3.5: What the principles mean for businesses in practice

A fictional company, 'Good AI Recruitment Limited', provides recruitment services that use a range of AI systems to accelerate the recruitment process, including a service that automatically shortlists candidates based on application forms. While potentially useful, such systems may discriminate against certain groups that have historically not been selected for certain positions.

After the implementation of the UK's new AI regulatory framework, the Equality and Human Rights Commission (EHRC) and the Information Commissioner Office (ICO) will be supported and encouraged to work with the Employment Agency Standards Inspectorate (EASI) and other regulators and organisations in the employment sector to issue joint guidance. The joint guidance could address the cross-cutting principles relating to fairness, appropriate transparency and explainability, and contestability and redress in the context of the use of AI systems in recruitment or employment. Such joint guidance could, for example, make things clearer and easier for Good AI Recruitment Limited by:

1. Clarifying the type of information businesses should provide when implementing such systems

2. Identifying appropriate supply chain management processes such as due diligence or AI impact assessments

3. Suggesting proportionate measures for bias detection, mitigation and monitoring

4. Providing suggestions for the provision of contestability and redress routes.

Good AI Recruitment Limited would also be able to apply a variety of tools for trustworthy AI, such as technical standards, that would supplement regulatory guidance and other measures promoted by regulators. In their published guidance regulators could, where appropriate, refer businesses to existing technical standards on transparency (such as IEEE 7001-2021), as well as standards on bias mitigation (such as ISO/IEC TR 24027:2021).

By following this guidance Good AI Recruitment Limited would be able to develop and deploy their services responsibly.

3.2.4 Our preferred model for applying the principles

55. Initially, the principles will be issued by government on a non-statutory basis and applied by regulators within their remits. We will support regulators to apply the principles using the powers and resources available to them. This initial period of implementation will provide a valuable opportunity to ensure that the principles are effective and that the wider framework is supporting innovation while addressing risks appropriately.

56. While industry has strongly supported non-statutory measures in the first instance, favouring flexibility and fewer burdens, some businesses and regulators have suggested that government should go beyond a non-statutory approach to ensure the principles have the desired impact.[footnote 108] Some regulators have also expressed concerns that they lack the statutory basis to consider the application of the principles. We are committed to an approach that leverages collaboration with our expert regulators but we agree that we may need to intervene further to ensure that our framework is effective.

57. Following a period of non-statutory implementation, and when parliamentary time allows, we anticipate that we will want to strengthen and clarify regulators' mandates by introducing a new duty requiring them to have due regard to the principles. Such a duty would give a clear signal that we expect regulators to act and support coherence across the regulatory landscape, ensuring that the framework displays the characteristics that we have identified.[footnote 109] One of the strengths of this approach is that regulators would still be able to exercise discretion and expert judgement regarding the relevance of each principle to their individual domains.

58. A duty would ensure that regulators retain the ability to exercise judgement when applying the principles in particular contexts - benefiting from some of the flexibility expected through non-statutory implementation. For example, while the duty to have due regard would require regulators to demonstrate that they had taken account of the principles, it may be the case that not every regulator will need to introduce measures to implement every principle. In having due regard to a particular principle, a regulator may exercise their expert judgement and determine that their sector or domain does not require action to be taken. The introduction of the duty will, however, give regulators a clear mandate and incentive to apply the principles where relevant to their sectors or domains.

59. If our monitoring of the effectiveness of the initial, non-statutory framework suggests that a statutory duty is unnecessary, we would not introduce it. Similarly, we will monitor whether particular principles cannot be, or are not being, applied in certain circumstances or by specific regulators because of the interpretation of existing legal requirements or because of technical constraints. Such circumstances may require broader legislative changes. Should we decide there is a need for statutory measures, we will work with regulators to review the interaction of our principles with their existing duties and powers.
Consultation questions:
7. Do you agree that introducing a statutory duty on regulators to have due regard to the principles would clarify and strengthen regulators' mandates to implement our principles while retaining a flexible approach to implementation?

8. Is there an alternative statutory intervention that would be more effective?

3.2.5 The role of individual regulators in applying the principles

60. In some sectors, principles for AI governance will already exist and may even go further than the cross-cutting principles we propose. Our framework gives sectors the ability to develop and apply more specific principles to suit their own domains, where government or regulators identify these are needed.

62. Not all principles will be equally relevant in all contexts and sometimes two or more principles may come into conflict. For example, it may be difficult to assess the fairness of an algorithm's outputs without access to sensitive personal data about the subjects of the processing. Regulators will need to use their expertise and judgement to prioritise and apply the principles in such cases, sharing information where possible with government and other regulators about how they are assessing the relevance of each principle. This collaboration between regulators and government will allow the framework to be adapted to ensure it is practical, coherent and supporting innovation.

63. In implementing the new regulatory framework we expect that regulators will:

Assess the cross-cutting principles and apply them to AI use cases that fall within their remit.

Issue relevant guidance on how the principles interact with existing legislation to support industry to apply the principles. Such guidance should also explain and illustrate what compliance looks like.

Support businesses operating within the remits of multiple regulators by collaborating and producing clear and consistent guidance, including joint guidance where appropriate.

64. Regulators will need to monitor and evaluate their own implementation of the framework and their own effectiveness at regulating AI within their remits. We understand that there may be AI-related risks that do not clearly fall within the remits of the UK's existing regulators.[footnote 110] Not every new AI-related risk will require a regulatory response and there is a growing ecosystem of tools for trustworthy AI that can support the application of the cross-cutting principles. These are described further in part 4.

65. Where prioritised risks fall within a gap in the legal landscape, regulators will need to collaborate with government to identify potential actions. This may include identifying iterations to the framework such as changes to regulators' remits, updates to the Regulators' Code,[footnote 111] or additional legislative interventions. Our approach benefits from our strong sovereign parliamentary system, which reliably allows for the introduction of targeted and proportionate measures in response to emerging issues, including by adapting existing legislation if necessary.[footnote 112]

66. The Sir Patrick Vallance review has highlighted that rushed attempts to regulate AI too early would risk stifling innovation.[footnote 113] Our approach aligns with this perspective. We recognise the need to build a stronger evidence base before making decisions on statutory interventions. In doing so, we will ensure that we strike the right balance between retaining flexibility in our iterative approach and providing clarity to businesses. As detailed in section 3.3.1, we will deliver a range of central functions, including horizon scanning and risk monitoring, to identify and respond to situations where prioritised risks are not adequately covered by the framework, or where gaps between regulators' remits are negatively impacting innovation.

Case study 3.6: Responding to regulatory policy challenges - self-driving vehicles

Some aspects of a new AI use case may sit outside regulators' existing remits, meaning they do not have a mandate to address specific harms or support a new product to enter the market.

The advent of self-driving vehicles highlighted such a regulatory and policy challenge. Where sophisticated AI-enabled software is capable of performing the designated driving task, existing regulatory structures - where responsibility for road safety is achieved by licensing human drivers - are not fit for purpose. This creates uncertainty regarding the development and deployment of self-driving vehicles that cannot be addressed by regulators alone.

To achieve the government's ambition to 'make the UK one of the best places in the world to develop and deploy self-driving vehicles technology',[footnote 114] manufacturers need clarity about the regulatory landscape they are operating in and the general public needs to have confidence in the safety, fairness and trustworthiness of these vehicles.

The government published its Connected & Automated Mobility 2025 report[footnote 115] to address this challenge, describing how the ecosystem could be adapted to spur innovation and secure the economic and social benefits of this technology.

The work of the UK's Centre for Connected and Autonomous Vehicles is an example of government acting to identify regulatory gaps, develop policy and build UK capabilities. A central monitoring and evaluation function, described below, will identify and assess gaps in the regulatory ecosystem that could stifle AI innovation so that government can take action to address them.

3.2.6 Guidance to regulators on applying the principles

67. The proposed regulatory framework is dependent upon the implementation of the principles by our expert regulators. This regulator-led approach has received broad support from across industry, with stakeholders acknowledging the importance of the sector-specific expertise held by individual regulators. We expect regulators to collaborate proactively to achieve the best outcomes for the economy and society. We will work with regulators to monitor the wider framework and ensure that this collaborative approach to implementation is effective. If improvements are needed, including interventions to drive stronger collaboration across regulators, we will take further action.

68. Our engagement with regulators and industry highlighted the need for central government to support regulators. We will work with regulators to develop guidance that helps them implement the principles in a way that aligns with our expectations for how the framework should operate. Existing legal frameworks already mandate and guide regulators' actions. For example, nearly all regulators are bound by the Regulators' Code[footnote 116] and all regulators - as public bodies - are required to comply with the Human Rights Act.[footnote 117] Our proposed guidance to regulators will seek to ensure that when applying the principles, regulators are supported and encouraged to:

Adopt a proportionate approach that promotes growth and innovation by focusing on the risks that AI poses in a particular context.

Consider proportionate measures to address prioritised risks, taking into account cross-cutting risk assessments undertaken by, or on behalf of, government.

Design, implement and enforce appropriate regulatory requirements and, where possible, integrate delivery of the principles into existing monitoring, investigation and enforcement processes.

Develop joint guidance, where appropriate, to support industry compliance with the principles and relevant regulatory requirements.

Consider how tools for trustworthy AI like assurance techniques and technical standards can support regulatory compliance.

Engage proactively and collaboratively with government's monitoring and evaluation of the framework.

Case study 3.7: What this means for businesses

A fictional company, 'AI Fairness Insurance Limited', has delayed the deployment of a new AI application as - under the current patchwork of relevant regulatory requirements - it has been challenging to identify appropriate compliance actions for AI-driven insurance products.

Following implementation of the UK's new pro-innovation framework to regulate AI, we could expect to see joint guidance produced collaboratively by the Information Commissioner's Office (ICO), Equality and Human Rights Commission (EHRC), Financial Conduct Authority (FCA) and other relevant regulatory authorities. This would provide greater clarity on the regulatory requirements relevant to AI as well as guidance on how to satisfy those requirements in the context of insurance and consumer-facing financial services.

Under the proposed regulatory framework, AI Fairness Insurance Limited could be supported by new or updated guidance issued by regulators to address the AI regulatory principles. The company may also be able to follow joint regulatory guidance, issued as a result of collaboration between regulators, and use a set of tools provided by regulators, such as template risk assessments and transparency measures, and relevant technical standards (for example, international standards for transparency and bias mitigation). The collaboration between regulators and focus on practical implementation measures will guide the responsible deployment of AI Fairness Insurance Limited's AI product by making it easier for the company to navigate the regulatory landscape and address specific risks such as discrimination.

69. Further details about the implementation of the regulatory framework will be provided through an AI regulation roadmap which will be published alongside the government response to the consultation on this white paper.

3.3.1 New central functions to support the framework

70. Government has a responsibility to make sure the regulatory framework operates proportionately and supports innovation. Feedback to our proposals from businesses has been clear that the current patchwork of regulation, with relatively little in the way of central coordination or oversight, will create a growing barrier to innovation if left unaddressed. Responses from over 130 organisations and individuals to our 2022 policy paper highlighted the need for a greater level of monitoring and coordination to achieve the coherence and improved clarity we need to support innovation. Businesses, particularly small to medium sized enterprises, noted that regulatory coordination could improve business certainty and investment, resulting in more and better jobs in the sector.

71. Government therefore intends to put mechanisms in place to coordinate, monitor and adapt the framework as a whole. Further detail on these functions is set out below. Enhanced monitoring activity will allow us to take a structured approach to gathering feedback from industry on the impact of our regime as it is introduced. These mechanisms will supplement and support the work of regulators, without undermining their independence. Equally, such mechanisms are not intended to duplicate existing activities.

72. Delivering some functions centrally provides government with an overarching view of how the framework is working, where it is effective and where it may need improving. A central suite of functions will also facilitate collaboration by bringing together a wide range of interested parties, including regulators, international partners, industry, civil society organisations such as trade unions and advocacy groups, academia and the general public. Our engagement with these groups has highlighted the need for our proposed central functions. We will continue to convene a wide range of stakeholders to ensure that we hear the full spectrum of viewpoints. This breadth of engagement and collaboration will be integral to government's ability to monitor and improve the framework. The functions will identify and support opportunities for further coordination between regulators, resulting in greater clarity for businesses and stronger consumer trust.

73. We have identified a set of functions that will drive regulatory coherence and support regulators to implement the pro-innovation approach that we have outlined. These functions have been informed by our discussions with industry, research organisations, and regulators following the publication of the AI policy paper.

Box 3.1: Functions required to support implementation of the framework

Monitoring, assessment and feedback[footnote 118]
Activities Rationale Support coherent implementation of the principles Activities Rationale Cross-sectoral risk assessment Activities Rationale Support for innovators (including testbeds and sandboxes as detailed in section 3.3.4) Activities Rationale Education and awareness Activities Rationale Horizon scanning Activities Rationale Ensure interoperability with international regulatory frameworks Activities Rationale Consultation questions: [footnote 119]
9. Do you agree that the functions outlined in Box 3.1 would benefit our AI regulation framework if delivered centrally?

10. What, if anything, is missing from the central functions?

11. Do you know of any existing organisations who should deliver one or more of our proposed central functions?

12. Are there additional activities that would help businesses confidently innovate and use AI technologies?

12.1. If so, should these activities be delivered by government, regulators or a different organisation?

13. Are there additional activities that would help individuals and consumers confidently use AI technologies?

13.1. If so, should these activities be delivered by government, regulators or a different organisation?

14. How can we avoid overlapping, duplicative or contradictory guidance on AI issued by different regulators?

Box 3.2: Supporting coherence in risk assessment

Many AI risks do not fall neatly into the remit of one individual regulator and they could go unaddressed if not monitored at a cross-sector level. A central, cross-economy risk function will also enable government to monitor future risks in a rigorous, coherent and balanced way. This will include 'high impact but low probability' risks such as existential risks posed by artificial general intelligence or AI biosecurity risks.

A pro-innovation approach to regulation involves tolerating a certain degree of risk rather than intervening in all cases. Government needs the ability to assess and prioritise AI risks, ensuring that any intervention is proportionate and consistent with levels of risk mitigation activity elsewhere across the economy or AI life cycle.
What?
Establishing a central risk function will bring coherence to the way regulators and industry think about AI risk. It will also foster collaboration between government, regulators, industry and civil society to provide clarity for businesses managing AI risk across sectors.
How?
The central risk function will identify, assess, prioritise and monitor cross-cutting AI risks that may require government intervention.

The central risk function will bring together cutting-edge knowledge from industry, regulators, academia and civil society - including skilled computer scientists with a deep technical understanding of AI.

Given the importance of risk management expertise, we will seek inspiration and learning from sectors where operational risk management is highly developed. This will include looking for examples of how failures and near misses can be recorded and used to inform good practice.

Regulators will have a key role in designing the central risk framework and ensuring alignment with their existing practices. Where a risk that has been prioritised for intervention falls outside of any existing regulator's remit, the central risk function will identify measures that could be taken to address the gap (for example, updates to regulatory remits). The central risk function will also support smaller regulators that lack technical AI expertise to better understand AI risks.

Figure 2: Central risks function activities

Box 3.3: How a central monitoring and evaluation (M&E) function enables a proportionate, adaptable approach
What?
We will need to monitor the implementation of the framework closely to make sure that it is working as designed. We will monitor the regime to ensure it is pro-innovation, proportionate, adaptable, trustworthy, clear and collaborative - our desired characteristics.

The central M&E function will gather evidence and feedback from a range of sources and actors in the ecosystem. For example, effective M&E of the whole framework is likely to require input and data from industry, regulators, civil society, academia, international partners and the general public. Insights from regulatory sandboxes and testbeds as well as wider monitoring of the AI ecosystem as a whole will also be valuable.[footnote 122]

Government's ability to access reliable, comprehensive data and insights for the purposes of monitoring the AI regulatory framework will be closely related to our work raising awareness and educating businesses and consumers on AI-related issues. It is important for our M&E data to be drawn from a wide range of sources, reflecting the full spectrum of views and including seldom heard voices from the general public. Raising awareness and educating stakeholder groups will help to ensure that the broader conversation is inclusive, informed and rigorous.

We will develop and monitor metrics that demonstrate whether the framework is working as intended. For example, the central M&E function will look at the effectiveness of the framework in mitigating unacceptable risks and assess whether the implementation of the principles by regulators is disproportionate or negatively affecting innovation.
How?
Insights from the M&E function will contribute to the adaptability of our framework by enabling government to identify opportunities for improvement so we can benefit fully from the flexibility we have built into our approach. Such iteration could include removing or amending existing regulation as well as updating the AI regulatory framework itself.

The range, sources and quality of the data that informs our monitoring and evaluation of the framework will be critical.

The M&E function will identify the metrics and data sources to help us measure how well the regime is working, both in terms of the framework's ability to mitigate risk but also to ensure that it is supporting innovation. It will bring together a wide range of views including industry, civil society groups and academia.
Consultation questions:
Crucially, we will work with regulators to identify how their work - including data collected from their own regulatory activities - can support our central M&E function in order to ensure the best outcomes for the whole economy.

15. Do you agree with our overall approach to monitoring and evaluation?

16. What is the best way to measure the impact of our framework?

17. Do you agree that our approach strikes the right balance between supporting AI innovation; addressing known, prioritised risks; and future-proofing the AI regulation framework?

75. Government will initially be responsible for delivering the central functions described above, working in partnership with regulators and other key actors in the AI ecosystem to leverage existing activities where possible. This is aligned with our overall iterative approach and enables system-wide review of the framework. We recognise that there may be value in a more independent delivery of the central functions in the longer term.

76. Where relevant activities are already undertaken by organisations either within or outside of government, the primary role of the central functions will be to leverage these activities and assess their effectiveness. Where this is not the case - for example, where new bespoke capabilities are needed to monitor and evaluate the operation of the framework as a whole - these functions will initially be established in government.

Case study 3.8: Building on a strong foundation of regulatory coordination

The growth of digital technologies requires regulators to coordinate and act cohesively. The Digital Regulation Cooperation Forum (DRCF) has published its vision for a joined-up approach to digital regulation. It conducts cross-regulator horizon scanning for future technology and has issued detailed discussion papers on the benefits, harms and auditing of algorithms.

Regulators are also exploring ways to provide simpler 'shop fronts' for those they regulate, with the NHS AI and Digital Regulations Service offer already robustly tested with end users and now widely available.[footnote 123] DRCF regulators have a multi-agency advice service for digital innovators pilot underway, supported by government's Regulators' Pioneer Fund,[footnote 124] which aims to make it easier for firms operating across digital regulatory boundaries to do business.

Existing regulatory forums may need to be supplemented or adapted to successfully implement the cross-cutting principles. We will work in partnership with existing bodies as well as industry to improve and enhance regulatory coordination.

77. We are deliberately taking an iterative approach to the delivery of the regulatory framework and we anticipate that the model for providing the central functions will develop over time. We will identify where existing structures may need to be supplemented or adapted. In particular, we are focused on understanding:

Whether existing regulatory forums could be expanded to include the full range of regulators involved in the regulation of AI or whether additional mechanisms are needed.

What additional expertise government may need to support the implementation and monitoring of the principles, including the potential role that could be played by established advisory bodies.

The most effective way to convene input from across industry and consumers to ensure a broad range of opinions.

Consultation questions:
79. As the regulatory framework evolves and we develop a clearer understanding of the system-level functions that are needed, we will review the operational model outlined above. In particular, we will consider if a government unit is the most appropriate mechanism for delivering the central functions in the longer term or if an independent body would be more effective.

18. Do you agree that regulators are best placed to apply the principles and government is best placed to provide oversight and deliver central functions?

3.3.2 Government's role in addressing accountability across the life cycle

81. However, AI supply chains can be complex and opaque, making effective governance of AI and supply chain risk management difficult. Inappropriate allocation of AI risk, liability, and responsibility for AI governance throughout the AI life cycle and within AI supply chains could impact negatively on innovation. For example, inappropriate allocation of liability to a business using, but not developing, AI could stifle AI adoption. Similarly, allocating too much responsibility to businesses developing foundation models, on the grounds that these models could be used by third parties in a range of contexts, would hamper innovation.
Consultation questions:
82. We recognise the need to consider which actors should be responsible and liable for complying with the principles, which may not be the same actors who bear the burden under current legal frameworks. For example, data protection law differentiates between data controllers and data processors. Similarly, product safety laws include the concepts of producers and distributors. In the context of those specific legal frameworks, liability for compliance with various existing legal obligations is allocated by law to those identified supply chain actors. It is not yet clear how responsibility and liability for demonstrating compliance with the AI regulatory principles will be or should ideally be, allocated to existing supply chain actors within the AI life cycle.

L1. What challenges might arise when regulators apply the principles across different AI applications and systems? How could we address these challenges through our proposed AI regulatory framework?

L2.1 Do you agree that the implementation of our principles through existing legal frameworks will fairly and effectively allocate legal responsibility for AI across the life cycle?

L.2.2. How could it be improved, if at all?

L3. If you work for a business that develops, uses, or sells AI, how do you currently manage AI risk including through the wider supply chain? How could government support effective AI-related risk management?

3.3.3 Foundation models and the regulatory framework

86. Foundation models are an emerging type of general purpose AI that are trained on vast quantities of data and can be adapted to a wide range of tasks. The fast-paced development of foundation models brings novel challenges for governments seeking to regulate AI. Despite high levels of interest in the topic, the research community has not found a consensus on how foundation models work, the risks they pose or even the extent of their capabilities.[footnote 126]

87. Foundation models have been described as paradigm shifting and could have significant impacts on society and the economy.[footnote 127] They can be used for a wide variety of purposes and deployed in many already complex ecosystems. Given the widely acknowledged transformative potential of foundation models, we must give careful attention to how they might interact with our proposed regulatory framework. Our commitment to an adaptable, proportionate approach presents a clear opportunity for the UK to lead the global conversation and set global norms for the future-proof regulation of foundation models.

88. There is a relatively small number of organisations developing foundation models. Some organisations exercise close control over the development and distribution of their foundation models. Other organisations take an open-source approach to the development and distribution of the technology. Open-source models can improve access to the transformational power of foundation models, but can cause harm without adequate guardrails.[footnote 128] The variation in organisational approaches to developing and supplying foundation models introduces a wide range of complexities for the regulation of AI. The potential opacity of foundation models means that it can also be challenging to identify and allocate accountability for outcomes generated by AI systems that rely on or integrate them.

89. Our proposed framework considers the issues raised by foundation models in light of our life cycle accountability analysis, outlined in section 3.3.2 above. Given the small number of organisations supplying foundation models and a proportionately larger number of businesses integrating or otherwise deploying foundation models elsewhere in the AI ecosystem, we recognise the important role of tools for trustworthy AI, including assurance techniques and technical standards.

90. The proposed central functions described in section 3.3.1 will play an important role in informing our approach to regulating foundation models. The central risk function's proactive, rigorous monitoring of risks associated with foundation models and the horizon scanning function's identification of related opportunities will be critical to ensuring that we strike the balance needed as part of our proportionate, pro-innovation regulatory approach. It will be crucial to ensure that the proposed monitoring and evaluation function has access to the technical skills and capabilities needed to assess the impact that our framework has on the opportunities and risks presented by foundation models.

91. We recognise that industry, academia, research organisations and global partners are looking for ways to address the challenges related to the regulation of foundation models.[footnote 129] For example, we know that developers of foundation models are exploring ways to embed alignment theory into their models. This is an important area of research, and government will need to work closely with the AI research community to leverage insights and inform our iteration of the regulatory framework. Our collaborative, adaptable framework will draw on the expertise of those researchers and other stakeholders as we continue to develop policy in this evolving area.

92. The UK is committed to building its capabilities in foundation models. Our Foundation Model Taskforce announced in the Integrated Review Refresh 2023[footnote 130] will support government to build UK capability and ensure the UK harnesses the benefits presented by this emerging technology. Our proposed framework will ensure we create the right regulatory environment as we move to maximise the transformative potential of foundation models.

Case-study 3.9: Life cycle accountability for large language models

Large language models (LLMs) are a type of foundation model.[footnote 131] The potential of LLMs goes beyond reproducing or translating natural language: LLMs also have the power to write software,[footnote 132] generate stories[footnote 133] through films and virtual reality,[footnote 134] and more.[footnote 135]

LLMs fall within the scope of our regulatory framework as they are autonomous and adaptable.

We are mindful of the rapid technological change in the development of foundation models such as LLMs and the new opportunities that they bring to applications including search engines, medical devices, and financial and legal services. However, LLMs also have limitations, for example, the models are not trained on a sense of truth,[footnote 136] so they can reproduce inconsistent or false outputs that seem highly credible.[footnote 137] Because they can be adapted to a wide variety of tasks downstream within an AI supply chain, any improvements or defects in a foundation model could quickly affect all adapted products.

Under the UK's pro-innovation AI regulatory framework, regulators may decide to issue specific guidance and requirements for LLM developers and deployers to address risks and implement the cross-cutting principles. This could include guidance on appropriate transparency measures to inform users when AI is being used and the data used to train the model.

The wide-reaching impact of LLMs through the AI supply chain - together with their general purpose and potential wide ranging application - means they are unlikely to be directly 'caught' within the remit of any single regulator. This makes effective governance and supply chain risk-management challenging where LLMs are involved. The AI regulatory framework's monitoring and evaluation function will therefore need to assess the impacts of LLMs. The cross-cutting accountability and governance principle will encourage regulators and businesses to find ways to demonstrate accountability and good governance in responsible LLM development and use.

At this point it would be premature to take specific regulatory action in response to foundation models including LLMs. To do so would risk stifling innovation, preventing AI adoption, and distorting the UK's thriving AI ecosystem.

However, we are mindful of the rapid rate of advances in the power and application of LLMs, and the potential creation of new or previously unforeseen risks. As such, LLMs will be a core focus of our monitoring and risk assessment functions and we will work with the wider AI community to ensure our adaptive framework is capable of identifying and responding to developments relating to LLMs.

For example, one way of monitoring the potential impact of LLMs could be by monitoring the amount of compute used to train them, which is much easier to assess and govern than other inputs such as data, or talent. This could involve statutory reporting requirements for models over a certain size. This metric could become less useful as a way of establishing who has access to powerful models if machine learning development becomes increasingly open-source.[footnote 138]
Consultation questions:
Life cycle accountability - including the allocation of responsibility and liability for risks arising from the use of foundation models including LLMs - is a priority area for ongoing research and policy development. We will explore the ways in which technical standards and other tools for trustworthy AI can support good practices for responsible innovation across the life cycle and supply chain. We will also work with regulators to ensure they are appropriately equipped to engage with actors across the AI supply chain and allocate legal liability appropriately.

F1. What specific challenges will foundation models such as large language models (LLMs) or open-source models pose for regulators trying to determine legal responsibility for AI outcomes?

F2. Do you agree that measuring compute provides a potential tool that could be considered as part of the governance of foundation models?

F3. Are there other approaches to governing foundation models that would be more effective?

3.3.4 Artificial intelligence sandboxes and testbeds

93. Government is committed to supporting innovators by addressing regulatory challenges that prevent new, cutting-edge products from getting to market. Barriers can be particularly high when a path to market requires interaction with multiple regulators or regulatory guidance is nascent. Sir Patrick Vallance's Digital Report recommends that government works with regulators to develop an AI sandbox to support innovators. At the Budget, government confirmed our commitment to taking forward this recommendation.[footnote 139]

94. The Information Commissioner's Office ( ICO) and the Financial Conduct Authority ( FCA) have already successfully piloted digital sandboxes in their sectors.[footnote 140] The FCA sandbox has worked with over 800 businesses and accelerated their speed to market by an estimated 40% on average.[footnote 141] Sandbox participation has also been found to have significant financial benefits, particularly for smaller organisations.[footnote 142] We have heard from regulators, including those with less experience of taking part in previous initiatives, that they are keen to participate in new AI sandboxes to support their regulated sectors.

95. Regulatory sandboxes and testbeds will play an important role in our proposed regulatory regime. Such initiatives enable government and regulators to:

Support innovators to get novel products and services to market faster, so they can start generating economic and social benefits.

Test how the regulatory framework is operating in practice and illuminate unnecessary barriers to innovation that need to be addressed.

Identify emerging technology and market trends to which our regulatory framework may need to adapt.

96. To deliver an effective sandbox, we would like to understand more deeply what service focus would be most useful to industry. We are considering 4 options:

Single sector, single regulator: support innovators to bring AI products to the market in collaboration with a single regulator, focusing on only one chosen industry sector.[footnote 143]

Multiple sectors, single regulator: support AI innovators in collaboration with a single regulator that is capable of working across multiple industry sectors.[footnote 144]

Single sector, multiple regulator: establish a sandbox that only operates in one industry sector but is capable of supporting AI innovators whose path to market requires interaction with one or more regulators operating in that sector.[footnote 145]

Multiple sectors, multiple regulators: a sandbox capable of operating with one or more regulators in one or more industry sectors to help AI innovators reach their target market. The DRCF is piloting a version of this model.[footnote 146]

97. We intend to focus an initial pilot on a single sector, multiple regulator sandbox. Recognising the importance of AI innovations that have implications in multiple sectors (like generative AI models), we would look to expand this capability to cover multiple industry sectors over time.

98. Initially, we envisage focusing the sandbox on a sector where there is a high degree of AI investment, industry demand for a sandbox, and appetite for improved collaboration between regulators to help AI innovators take their products to market. We invite consultation feedback on this proposal as well as suggestions for industry sectors that meet these criteria.

99. We would also like to build a deeper understanding of what service offering would be most helpful to industry. Some sandboxes offer supervised real-life or simulated test environments where innovators can trial new products, often under relaxed regulatory requirements.[footnote 147] In other scenarios, a team of technologists and regulation experts give customised advice and support to participating innovators over a number of months to help them understand and overcome regulatory barriers so they can reach their target market.[footnote 148] Our current preference is for the customised advice and support model, as we think this is where we can deliver benefits most effectively in the short term. We will explore options for developing a safe test environment capability at a later date, informed by our initial pilot work.
Consultation questions:
100. The implementation of an AI regulatory sandbox will also be closely informed by Sir Patrick Vallance's review into digital regulation and his recommendation to establish a multi-regulator sandbox.[footnote 149] The review sets out a number of design principles, which we will build into our pilot approach. This includes targeting such initiatives at start-ups and small to medium-sized businesses. As a matter of priority, we will engage with businesses to understand how such an approach should be designed and delivered to best support their needs.

S1. To what extent would the sandbox models described in section 3.3.4 support innovation?

S2. What could government do to maximise the benefit of sandboxes to AI innovators?

S3. What could government do to facilitate participation in an AI regulatory sandbox?

S4. Which industry sectors or classes of product would most benefit from an AI sandbox?

3.3.5 Regulator capabilities

101. Government has prioritised the ongoing assessment of the different capability needs across the regulatory landscape. We will keep this under close review as part of our ongoing monitoring and evaluation activity.

102. While our approach does not currently involve or anticipate extending any regulator's remit,[footnote 150] regulating AI uses effectively will require many of our regulators to acquire new skills and expertise. Our research[footnote 151] has highlighted different levels of capability among regulators when it comes to understanding AI and addressing its unique characteristics. Our engagement has also elicited a wide range of views on the capabilities regulators require to address AI risks and on the best way for regulators to acquire these.
AI expertise. Particularly:
103. We identified potential capability gaps among many, but not all, regulators, primarily in relation to:
Organisational capacity. A regulator's ability to:

Technical expertise in AI technology.[footnote 152] For example, on how AI is being used to deliver products and services and on the development, use and applicability of technical standards.[footnote 153]

Expertise on how AI use cases interact across multiple regulatory regimes.

Market intelligence on how AI technologies are being used to disrupt existing business models, both in terms of the potential opportunities and risks that can impact regulatory objectives.

Effectively adapt to the emergence of AI use cases and applications, and assimilate and share this knowledge throughout the organisation.

Work with organisations that provide assurance techniques (such as assurance service providers) and develop technical standards (such as standards development organisations), to identify relevant tools and embed them into the regulatory framework and best practice.

Work across regulators to share knowledge and cooperate in the regulation of AI use cases that interact across multiple regulatory regimes.

Establish relationships and communicate effectively with organisations and groups not normally within their remit.

104. In the initial phases of implementation, government will work collaboratively with key partners to leverage existing work on this topic. For example, the Digital Regulation Cooperation Forum ( DRCF) is already exploring ways of addressing capability gaps within its members.
Consultation questions:
105. There are options for addressing capability gaps within individual regulators and across the wider regulatory landscape, which we will continue to explore. It may, for example, be appropriate to establish a common pool of expertise that could establish best practice for supporting innovation through regulatory approaches and make it easier for regulators to work with each other on common issues. An alternative approach would be to explore and facilitate collaborative initiatives between regulators - including, where appropriate, further supporting existing initiatives such as the DRCF - to share skills and expertise.

19. As a regulator, what support would you need in order to apply the principles in a proportionate and pro-innovation way?

20. Do you agree that a pooled team of AI experts would be the most effective way to address capability gaps and help regulators apply the principles?

Part 4: Tools for trustworthy AI to support implementation

4.1 AI assurance techniques

106. Tools for trustworthy AI including assurance techniques and technical standards will play a critical role in enabling the responsible adoption of AI and supporting the proposed regulatory framework. Industry and civil society were keen to see a range of practical tools to aid compliance. Government is already supporting the development of these tools by publishing a Roadmap to an effective AI assurance ecosystem in the UK[footnote 154] and establishing the UK AI Standards Hub[footnote 155] to champion the use of technical standards.[footnote 156]

107. To assure AI systems effectively, we need a toolbox of assurance techniques to measure, evaluate and communicate the trustworthiness of AI systems across the development and deployment life cycle. These techniques include impact assessment, audit, and performance testing along with formal verification methods.

108. It is unlikely that demand for AI assurance can be entirely met through organisations building in-house capability. The emerging market for AI assurance services and expertise will have an important role to play in providing a range of assurance techniques to actors within the AI supply chain. There is an opportunity for the UK to become a global leader in this market as the AI assurance industry develops. This will enable organisations to determine whether AI technologies are aligned with relevant regulatory requirements.

4.2 AI technical standards

110. Assurance techniques need to be underpinned by available technical standards, which provide common understanding across assurance providers. Technical standards and assurance techniques will also enable organisations to demonstrate that their systems are in line with the UK's AI regulatory principles.

111. Multiple international and regional standards development organisations are developing, or have already released, AI-specific technical standards, addressing topics such as risk management, transparency, bias, safety and robustness. Accordingly, technical standards can be used by regulators to complement sector-specific approaches to AI regulation by providing common benchmarks and practical guidance to organisations.[footnote 157] Overall, technical standards can embed flexibility[footnote 158] into regulatory regimes and drive responsible innovation by helping organisations to address AI-related risks.[footnote 159]

112. The UK plays a leading role in the development of international technical standards, working with industry, international and UK partners.[footnote 160] The government will continue to support the role of technical standards in complementing our approach to AI regulation, including through the UK AI Standards Hub.

Box 4.1: Supporting a layered approach to AI technical standards

The government will complement its context-specific approach to AI regulation by proposing a proportionate 'layered approach' to applying available AI technical standards. This involves regulators identifying relevant technical standards and encouraging their adoption by actors in the AI life cycle to support the integration of the AI regulation principles into technical and operational business processes:

Layer 1: To provide consistency and common foundations across regulatory remits, in the first instance regulators could seek to encourage adoption of sector-agnostic standards which can be applied across AI use cases to support the implementation of cross-sectoral principles. For example, management systems, risk management, and quality standards[footnote 161] can provide industry with good practices for the responsible development of AI systems. The adoption of these standards should be encouraged by multiple regulators as tools for regulated entities to establish common good practices for AI governance.

Layer 2: To adapt these governance practices to the specific risks raised by AI in a particular context, regulators could look at encouraging adoption of additional standards addressing specific issues such as bias and transparency.[footnote 162] Such standards would act as tools for industry to operationalise compliance with specific AI regulation principles. As these standards will provide good practices for AI governance applicable to multiple sectors, regulators could complement these with sector-specific guidance.

For example, standards for bias mitigation could be promoted by the Financial Conduct Authority (FCA) and the Equality and Human Rights Commission (EHRC) as practical tools for providers of AI scoring models to identify and mitigate relevant sources of bias to ensure the fairness of the outcomes when the AI model is applied to financial services (credit scoring) and HR practices (candidate scoring) respectively.
Consultation questions:
Layer 3: Where relevant, regulators could encourage adoption of sector-specific technical standards to support compliance with specific regulatory requirements and performance measures.[footnote 163]

21. Which non-regulatory tools for trustworthy AI would most help organisations to embed the AI regulation principles into existing business processes?

Part 5: Territorial application

5.1 Territorial application of the regulatory framework

114. We will continue to engage devolved administrations, businesses, and members of the public from across the UK to ensure that every part of the country benefits from our pro-innovation approach. We will, for example, convene the devolved administrations for views on the functions we expect the government to perform and on the potential implications of introducing a statutory duty on regulators to have due regard to the principles.

5.2 Extraterritorial application of the regulatory framework

115. While we expect our principles-based approach to influence the global conversation on AI governance, we are not currently proposing the introduction of new legal requirements. Our framework will not therefore change the territorial applicability of existing legislation relevant to AI (including, for example, data protection legislation).

Part 6: Global interoperability and international engagement

6.1 Our regulatory framework on the world stage

116. Countries and jurisdictions around the world are moving quickly to set the rules that govern AI. The UK is a global leader in AI with a strategic advantage that places us at the forefront of these developments. The UK is ranked third in the world for AI publications and also has the third highest number of AI companies.[footnote 164] We want to build on this position, making the UK the best place to research AI and to create and build innovative AI companies. At the same time, we recognise the importance of working closely with international partners. As such, the UK's approach to both our domestic regulation and international discussions will continue to be guided by our ambition to develop AI frameworks that champion our democratic values and economic priorities.

117. In line with our domestic approach, we will focus on supporting the positive global opportunities AI can bring while protecting citizens against the potential harms and risks that can emanate across borders. We will work closely with international partners to both learn from, and influence, regulatory and non-regulatory developments (see examples in box 6.1). Given the complex and cross-border nature of AI supply chains, with many AI businesses operating across multiple jurisdictions, close international cooperation will strengthen the impact of our proposed framework.

118. We will promote interoperability and coherence between different approaches, challenging barriers which may stand in the way of businesses operating internationally. We will ensure that the UK's regulatory framework encourages the development of a responsive and compatible system of global AI governance. We will build our international influence, allowing the UK to engage meaningfully with like-minded partners on issues such as cross-border AI risks and opportunities.

120. Our international approach will include ensuring that proven, effective, and agreed upon assurance techniques and international technical standards play a role in the wider regulatory ecosystem. Such measures will also support cross-border trade by setting out risk management and AI governance practices that are globally recognised by trading partners, reducing technical barriers to trade and increasing market access. We will also use our world-leading innovation provisions in Free Trade Agreements to address the challenges innovators in AI may face and ensure that businesses are able to take advantage of the opportunities it presents.

121. In multilateral engagements, we will work to leverage each forum's strengths, expertise and membership to ensure they are adding maximum value to global AI governance discussions and are relevant to our democratic values and economic priorities.

Box 6.1: Examples of international engagement and collaboration
Multilateral AI engagement

The UK has played an active and leading role on the international AI stage and will continue to do so. Some (non-exhaustive) examples of activities are:
Bilateral AI engagement

OECD AI Governance Working Party (AI-GO): The UK is an active member of the OECD's Working Party on AI Governance (AIGO), which supports the implementation of the OECD's AI principles and enables the exchange of experience and best practice to advance the responsible stewardship of AI.[footnote 165]

Global Partnership on AI (GPAI): The UK is a key contributor to - and founding member of - the Global Partnership on AI (GPAI), which is an independent organisation consisting of 29 countries and a range of international experts. GPAI was launched in 2020 as the first international multilateral forum to focus solely on AI and the UK has played a significant role in shaping its development and influencing its agenda.[footnote 166]
At the 2022 GPAI Ministerial Summit in Japan, we demonstrated the scale of the UK's AI ambitions by announcing £1.2m of funding to develop a Net Zero Data Space for AI Applications (which will also support our Net Zero policy objectives).[footnote 167] This is in addition to the previous £1m investment to advance GPAI research on data justice (collaborating with The Alan Turing Institute and 12 pilot partners in low and medium income countries).

G7: The UK is actively engaged in the G7's work on AI and we are working closely with Japan - which holds the G7 Presidency for 2023 - to encourage greater international collaboration, support the development of consistent, proportionate and interoperable regulatory interventions, and champion the role of tools for trustworthy AI where appropriate.

Council of Europe Committee on AI (CAI): The UK holds a Bureau position and we are working closely with like-minded nations on the proposed Convention on AI, to help protect human rights, democracy and rule of law.[footnote 168]

UNESCO: The UK was actively involved in the development of the UNESCO Ethics of AI Recommendations and UK organisations have been supporting the development of implementation tools.[footnote 169]

Global standards development organisations: The UK will continue to work with international partners and global standards development organisations to develop and promote global technical standards for AI, including through the UK AI Standards Hub.[footnote 170] For example, the UK is playing a leading role in the International Organisation for Standardisation and International Electrotechnical Commission[footnote 171] (ISO/IEC) on 4 active AI projects.[footnote 172] Through the British Standards Institution (BSI),[footnote 173] we are also a member of the Open Community for Ethics in Autonomous and Intelligent Systems (OCEANIS).[footnote 174]

The UK is engaging with individual nations and jurisdictions as they develop regulatory and governance approaches to AI. These include the European Union (and its Member States), US, Canada, Singapore, Japan, Australia, Israel, Norway, and Switzerland, amongst many others. We will continue to maintain close dialogues to share information and knowledge, learn from and adapt our approach in collaboration with others, and work together to shape the international landscape.

Part 7: Conclusion and next steps

7.1 Conclusion and next steps

122. Our proportionate approach to regulating AI is designed to strengthen the UK's position as a global leader in artificial intelligence, harness AI's ability to drive growth and prosperity,[footnote 175] and increase public trust in these technologies. The approach we set out is proportionate, adaptable, and context-sensitive to strike the right balance between responding to risks and maximising opportunities.

123. The proposals set out in this document have been informed by the feedback we received from over 130 respondents as part of our call for views on our 2022 policy paper. We will continue to work closely with businesses and regulators as we start to establish the central functions we have identified. Ongoing engagement with industry will be key to our monitoring and evaluation. Feedback will ensure the framework can adapt to new evidence, future-proofing the UK's role as a leader in AI innovation and ensuring that we can take a leading role in shaping the global narrative on AI regulation.

124. Given the pace at which AI technologies and risks emerge, and the scale of the opportunities at stake, we know that there is no time to waste if we are to strengthen the UK's position as one of the best places in the world to start an AI company. In collaboration with regulators, we are already exploring approaches to implementing the framework and will scale up this activity over the coming months. We are committed to an adaptable, iterative approach that allows us to learn and improve the framework. Our sovereign parliamentary system enables us to deliver targeted and proportionate measures - including by adapting existing legislation if necessary - based on emerging evidence.[footnote 176] There are therefore aspects of our implementation work that will be delivered in parallel with the wider consultation set out in this white paper.

125. In the first 6 months following publication we will:

126. In the 6 to 12 months after publication we will:

Agree partnership arrangements with leading organisations and existing initiatives to deliver the first central functions.

Encourage key regulators to publish guidance on how the cross-sectoral principles apply within their remit.

Publish proposals for the design of a central M&E framework including identified metrics, data sources, and any identified thresholds or triggers for further intervention or iteration of the framework. This will be published for consultation.

Continue to develop a regulatory sandbox or testbed with innovators and regulators.

127. In the longer-term, 12 months or more after publication, we will:
Consultation questions:

Deliver a first iteration of all the central functions required to ensure the framework is effective.

Work with key regulators that have not published guidance on how the cross-sectoral principles apply within their remit to encourage and support them to do so.

Publish a draft central, cross-economy AI risk register for consultation.

Develop the regulatory sandbox or testbed drawing on insights from the pilot.

Publish the first monitoring and evaluation report. This will evaluate how well the cross-sectoral principles are functioning and the delivery of the central functions. Performance will be measured against our framework characteristics: pro-innovation, proportionate, trustworthy, adaptable, clear and collaborative. The report will also consider existing regulatory activity and the role of government in supporting this, including whether appropriate guidance (including joint guidance) has been issued. In the report, we will include considerations on the need for any iteration of the framework, including the need for statutory interventions.

Publish an updated AI Regulation Roadmap which will set out plans for the future delivery of the central functions. In particular, it will assess whether a central government team is the most appropriate mechanism for overseeing the central functions in the longer term or if a more independent body would be more effective.

22. Do you have any other thoughts on our overall approach? Please include any missed opportunities, flaws, and gaps in our framework.

Annex A: Implementation of the principles by regulators

A.1 Factors that government believes regulators may wish to consider when providing guidance/implementing each principle

Principle

Implementation considerations

Safety, security and robustness

We anticipate that regulators will need to:

1. Provide guidance about this principle including:

(i) considerations of good cybersecurity practices, such as the NCSC principles for the security of machine learning,[footnote 177] as a secured system should be capable of maintaining the integrity of information.

(ii) considerations of privacy practices such as accessibility only to authorised users and safeguards against bad actors.

2. Refer to a risk management framework that AI life cycle actors should apply. Models should be regularly reviewed over time as a mitigation strategy.

3. Consider the role of available technical standards, for example addressing AI safety, security, testing, data quality, and robustness (including, , * , * , and *) to clarify regulatory guidance and support the implementation of risk treatment measures.

Appropriate transparency and explainability

We anticipate that regulators will need to:

1. Set expectations for AI life cycle actors to proactively or retrospectively provide information relating to:

(i) the nature and purpose of the AI in question including information relating to any specific outcome,

(ii) the data being used and information relating to training data,

(iii) the logic and process used and where relevant information to support explainability of decision-making and outcomes,

(iiii) accountability for the AI and any specific outcomes.

2. Set explainability requirements, particularly of higher risk systems, to ensure appropriate balance between information needs for regulatory enforcement (for example, around safety) and technical tradeoffs with system robustness.

3. Consider the role of available technical standards addressing AI transparency and explainability (such as IEEE 7001, )[footnote 178] to clarify regulatory guidance and support the implementation of risk treatment measures.

Appropriate transparency and explainability

We anticipate that regulators will need to:

1. Interpret and articulate 'fairness' as relevant to their sector or domain,

2. Decide in which contexts and specific instances fairness is important and relevant (which it may not always be).

3. Design, implement and enforce appropriate governance requirements for 'fairness' as applicable to the entities that they regulate.

4. Where a decision involving use of an AI system has a legal or similarly significant effect on an individual, regulators will need to consider the suitability of requiring AI system operators to provide an appropriate justification for that decision to affected parties.

5. AI systems should comply with regulatory requirements relating to vulnerability of individuals within specific regulatory domains. Regulators will need to consider how use of AI systems may alter individuals' vulnerability, pursuant to their existing powers and remits.

6. Consider the role of available technical standards addressing AI fairness, bias mitigation and ethical considerations (for example, , *, ) to clarify regulatory guidance and support the implementation of risk treatment measures.

Accountability and governance

We anticipate that regulators will need to:

1. Determine who is accountable for compliance with existing regulation and the principles. In the initial stages of implementation, regulators might provide guidance on how to demonstrate accountability. In the medium to long term, government may issue additional guidance on how accountability applies to specific actors within the ecosystem.

2. Provide guidance on governance mechanisms including, potentially, activities in scope of appropriate risk management and governance processes (including reporting duties).

3. Consider how available technical standards addressing AI governance, risk management, transparency and other issues can support responsible behaviour and maintain accountability within an organisation (for example, , , *).

Contestability and redress

We anticipate that regulators will need to:

1. Create or update guidance with relevant information on where to direct a complaint or dispute for those affected by AI harms. Guidance should clarify existing 'formal' routes of redress offered by regulators in certain scenarios.

2. Clarify interactions with requirements of appropriate transparency and explainability, acting as pre-conditions of effective redress and contestability.

Annex B: Stakeholder engagement

B.1 Summary

In July 2022, we published a policy paper outlining our proposals for Establishing a pro-innovation approach to regulating AI.[footnote 179] We proposed a non-statutory framework underpinned by a set of cross-sectoral principles including transparency, safety, and security. The principles were intended to guide how regulators approach AI risks. We outlined our intention for the framework to be coherent, proportionate and adaptable, with regulatory coordination to reduce burdens on business and agility to keep pace with rapid technological advancements. Our proposals were designed to strengthen the UK's position as a global leader in AI by ensuring the UK is the best place to develop and use AI technologies.

We launched a call for views on the proposals outlined in our policy paper to capture feedback from stakeholders between July and September 2022. We received responses from over 130 different stakeholders. There were some clear themes amongst the responses, with stakeholders noting the importance of regulatory coordination and asking for further details on how this will be achieved.

The 2023 AI regulation white paper sets out our latest position based on the feedback we received. In particular, we have considered the need for new central functions to undertake activities such as system-wide risk monitoring and evaluation of the AI regulation framework.

We welcome feedback on our latest proposals and will actively engage stakeholders as part of a consultation running to 21st June. See Annex C for more details on how to contribute to this consultation.

B.2 Background

In July 2022, we opened a public call for views on our policy paper: Establishing a pro-innovation approach to regulating AI. We invited stakeholder views on how the UK can best set the rules for regulating AI in a way that drives innovation and growth while also protecting our fundamental values. Feedback was collected to inform the development of the white paper.

We welcomed reflections on our proposed approach and specifically invited views and supporting evidence on the following questions:

1. What are the most important challenges with our existing approach to regulating AI? Do you have views on the most important gaps, overlaps or contradictions?

2. Do you agree with the context-driven approach delivered through the UK's established regulators set out in this paper? What do you see as the benefits of this approach? What are the disadvantages?

3. Do you agree that we should establish a set of cross-sectoral principles to guide our overall approach? Do the proposed cross-sectoral principles cover the common issues and risks posed by AI technologies? What, if anything, is missing?

4. Do you have any early views on how we best implement our approach? In your view, what are some of the key practical considerations? What will the regulatory system need to deliver on our approach? How can we best streamline and coordinate guidance on AI from regulators?

5. Do you anticipate any challenges for businesses operating across multiple jurisdictions? Do you have any early views on how our approach could help support cross-border trade and international cooperation in the most effective way?

6. Are you aware of any robust data sources to support monitoring the effectiveness of our approach, both at an individual regulator and system level?

The call for views and evidence was open for 10 weeks, closing on 26 September 2022. In this period we met with 39 stakeholders to capture detailed feedback on our proposals. In total, we received responses from over 130 stakeholders. Stakeholders represented a range of perspectives, from start-ups to Big Tech, and included developers, deployers, and funders from across the AI life cycle. We also heard from researchers, regulators, lawyers, trade bodies and unions as well as representatives from the devolved administrations, local government, and wider public sector.

We have carefully analysed all the views and evidence submitted. We are grateful for the time and effort our stakeholders committed during this process, which has informed and strengthened our policy position as outlined in the white paper.

B.3 Responses

Overall, there was strong support for context specific regulation implemented by existing regulators and many noted that this approach would drive innovation. Stakeholders felt our proposals were a proportionate way to establish regulatory best practice in a fast-changing landscape. However, responses also asked for more practical detail, particularly around risk tolerance, compliance measures, and the overall coherence of the framework.

Our analysis found 6 overarching themes raised by stakeholders:

1. Articulating the intended societal benefits of AI is key to a future-proofed regulatory vision that works for citizens as well as businesses.

Government response: We have analysed our principles in consideration of both stakeholder feedback and our risk assessment work. The white paper clarifies the substance of the principles in section 3.2.3. Human rights and environmental sustainability are not explicitly named in the revised principles as we expect regulators to adhere to existing law when implementing the principles. We have emphasised the social benefits alongside the economic opportunities we intend to unlock with our pro-innovation approach to AI regulation.

2. Offering greater central clarity around the scope of the regime is critical to ensuring business confidence

A number of stakeholders praised our description of AI for capturing the distinct regulatory challenges that AI poses and our proposed characteristics were largely considered to be fit for purpose. There were some concerns that the definition was not 'user-friendly' on its own. While many felt that creating a more specific definition of AI would be difficult and some noted it could be unhelpful, there was clear appetite for further detail on how regulators will maintain a coherent definition of AI within and across sectors. Use cases were suggested as a means of illustrating AI technologies within scope.

Many stakeholders, especially from industry, were keen to see a clear and transparent risk management framework with assessment criteria. In particular, multiple stakeholders felt that it would be beneficial for central government or a central body to provide a clear description of what constitutes 'unacceptable risk'. Some suggested this could complement more detailed risk analysis by regulators to ensure a coordinated and coherent approach - as well as effectively identifying any gaps. Stakeholders indicated that greater clarity on risk would support business development and could also promote high standards, public trust, and the adoption of AI.

Government response: We stress-tested our proposed characteristics of AI against stakeholder feedback and found that concerns centred on how we would ensure coherence across sectors and regulators. We recognise a trade-off between the certainty provided by a blanket approach, such as a singular definition and central risk framework, and the agility enabled by sector-specific expertise, including regulator-refined definitions. Given the fast pace of technological development and stakeholder praise for a future-proofed approach, we have retained our core, defining characteristics for AI, see section 3.2.1. We have considered how regulators can be given the technical capability necessary to create clear definitions for AI in and across their sectors, see section 3.2.1. In section 3.3.1 of the white paper, we outline how new central functions will help identify conflicts or gaps in regulator definitions of AI. Acknowledging feedback that a central steer on 'acceptable' risk would provide business confidence and investment, we have proposed that centralised risk monitoring and horizon scanning would be key central functions.

3. A principles-based approach will enable regulation to keep pace with a fast-evolving technology

Stakeholders generally agreed that a principles-based approach implemented by regulators would offer a proportionate way to build best practice. Stakeholders felt the principles address the key risks that AI poses while allowing regulators to tailor approaches to their sectors. Stakeholders welcomed our use of the OECD principles as a means of promoting international alignment and interoperability.

While stakeholders recognised the benefits that a flexible non-statutory approach offers, some stakeholders were concerned that a non-statutory approach would be unenforceable. A few stakeholders suggested clarifying how AI regulation dovetails with existing legislation and defining thresholds for when our regime may shift to statutory implementation.

Government response: We appreciate the praise of our adaptation of the multilaterally agreed OECD principles. We further outline our international approach in the white paper, recognising that interoperability will help ensure that UK businesses can continue to innovate. While we continue with a non-statutory approach for initial implementation, reflecting on stakeholder concerns around enforceability, we anticipate that introducing a statutory duty to have due regard on regulators might be needed to strengthen the framework. A duty to have due regard to our cross-sector principles will provide a legislative incentive while maintaining flexibility for the framework to adapt to technological changes. We will monitor the implementation of the framework to assess whether it is effective without the need to implement a statutory duty and will also review responses to the white paper consultation.

4. Providing centralised coordination and oversight will be essential to regulatory coherence and horizon scanning

Stakeholders voiced concerns that regulators did not have the capability to ensure a coherent compliance process, especially for businesses operating across or between industry sectors or regulatory remits. Stakeholders reported expensive, time-consuming confusion when there was not clear regulatory ownership of a technology or issue. Some criticised communication and knowledge-sharing between regulators. One stakeholder explained that joint guidance had previously been very useful. Others suggested that regulators should have more stringent duties to collaborate to ensure consistency and shared best practice.

A number of stakeholders were supportive of a central coordination function for existing regulators, as opposed to a new regulator for AI. Many stressed the importance of a coordination function to aid navigation of trade-offs and conflicts (such as between the need to collect data to minimise bias and the need to refrain from collecting data in the interest of privacy). While many stakeholders stated the need for central coordination, many were solution-agnostic. Proposals included:

An expanded role for the DRCF. Some stakeholders suggested the DRCF was well-positioned to take on a coordination function but others questioned the DRCF's suitability. In particular, it was felt that the DRCF would require more capacity to fulfil a coordination role.

A new central body to undertake coordination. Stakeholders suggested establishing a new body, such as a 'Centre for AI Governance', to undertake functions such as: conducting cross-sector risk-mapping; conducting regulatory gap analyses and horizon scanning; monitoring the applicability of emerging AI standards; supplying training; and monitoring international approaches.

Appointing an existing regulator as 'lead regulator' for AI. Some stakeholders felt that regulators should have more incentives to work together and the entire regulatory landscape could learn from more advanced regulators.

Stakeholders stated the importance of clarifying regulator remits and addressing gaps, noting the fast pace of change for AI technologies. Some suggested that a coordination body should be responsible for a horizon scanning function that monitors and evaluates risks.

Government response: Building on reflections from stakeholders, we identified a small range of regulators with remits that are likely to be significantly affected by AI and conducted analysis of their capability to implement our policy paper proposals. We found varied readiness, with some regulators already demonstrating world-leading approaches to regulating AI and others asking for further support. Similarly, knowledge and information sharing mechanisms were not uniform across regulators and we identified a need for coordination mechanisms to streamline compliance processes for business and ensure regulation provides system-wide coverage of current and future opportunities. We considered multiple options for coordination functions, in line with stakeholder suggestions, and incorporated feedback into our analysis. We outline our proposals for central functions in section 3.3.1 in the white paper.

5. Streamlining liability and tailoring reporting obligations will be key to enabling responsible innovation

While stakeholders were strongly supportive of compliance and assurance as a means of facilitating public trust and the wider adoption of AI technologies, many were keen to limit the burden of reporting obligations, particularly for startups and SMEs. Industry stakeholders noted that the costs of reporting burdens would be passed onto consumers. Some stakeholders emphasised that the government should have a role in providing education and support for small businesses.

There was interest in regulatory sandboxes as a way to enable investment and establish best practice. Generally there was a strong appetite for industry-led solutions and a less burdensome or 'tick box' approach to compliance. Stakeholders were strongly supportive of standards as a way to drive accountability, adoption, and good consumer outcomes. Stakeholders suggested sector compliance templates and voluntary industry forums as ways to share knowledge and reduce the burden of establishing best practice.

Some stakeholders felt the paper lacked a position on liability and argued a clear allocation of legal responsibility would enable effective enforcement and unlock investment. More specifically, some stakeholders suggested that, when appropriate, targeting foundation models (often developed by larger organisations) would increase innovation and competition by reducing liability burdens on smaller companies. Stakeholders often suggested impact assessments could be used to help address liability issues at all stages of the AI life cycle.

Government response: We welcomed the thoughtful suggestions from respondents regarding innovative compliance measures. We noted the significant appetite for regulatory sandboxes and have outlined our proposals in the white paper, see section 3.3.4. We agree that reporting burdens should be proportionate and give detail on how we will continue to work with regulators to ensure compliance measures are streamlined. We acknowledge that regulation measures can affect competition and innovation by creating undue burdens on start-ups and SMEs. We are confident that regulators will oversee proportionate and innovation friendly measures in their remits, with a central function undertaking activity to streamline and ensure coherence. We recognise that liability is complicated by a complex AI value-chain that can incorporate many different actors in different roles. As such, we believe that regulators are best positioned to begin allocating liability in their sectors, adopting a context-based approach that builds on best practice. Our proposal setting out activities to be undertaken centrally will ensure that regulators' approaches to liability are proportionate, coherent across sectors, and supportive of innovation.

6. Establishing interoperability will be critical to ensuring an internationally competitive approach

Government response: In the white paper, we set out our vision for AI regulation to ensure that the UK is the best place to start and grow an AI business. We share stakeholder concerns on interoperability and plan to continue using our leading role in international forums such as the OECD, G7, and Council of Europe to promote pro-innovation approaches to regulation that capitalise on the potential social and economic benefits of AI while addressing the new risks the technology can pose. Our plan for international engagement, detailed in part 6, clarifies our approach with an emphasis on interoperability.

Annex C: How to respond to this consultation

We are inviting individuals and organisations to provide their views by responding to the questions set out in this consultation. The questions are listed below.

The consultation will be open for 12 weeks, until 21 June.

You can respond online via the consultation survey link here.

Our privacy statement is set out in the following [link here].(https://www.gov.uk/government/publications/office-for-artificial-intelligence-information-collection-and-analysis-privacy-notice)

If for exceptional reasons, you are unable to use the online system, for example because you use specialist accessibility software that is not compatible with the system, you may request and complete a word document version of the form.
By post
By email
evidence@officeforai.gov.uk

Questions:

The revised cross-sectoral AI principles

1. Do you agree that requiring organisations to make it clear when they are using AI would adequately ensure transparency?

2. What other transparency measures would be appropriate, if any?

3. Do you agree that current routes to contestability or redress for AI-related harms are adequate?

4. How could routes to contestability or redress for AI-related harms be improved, if at all?

5. Do you agree that, when implemented effectively, the revised cross-sectoral principles will cover the risks posed by AI technologies?

6. What, if anything, is missing from the revised principles?

A statutory duty to regard

7. Do you agree that introducing a statutory duty on regulators to have due regard to the principles would clarify and strengthen regulators' mandates to implement our principles, while retaining a flexible approach to implementation?

8. Is there an alternative statutory intervention that would be more effective?

New central functions to support the framework

9. Do you agree that the functions outlined in section 3.3.1 would benefit our AI regulation framework if delivered centrally?

10. What, if anything, is missing from the central functions?

11. Do you know of any existing organisations who should deliver one or more of our proposed central functions?

12. Are there additional activities that would help businesses confidently innovate and use AI technologies?

12.1. If so, should these activities be delivered by government, regulators or a different organisation?

13. Are there additional activities that would help individuals and consumers confidently use AI technologies?

13.1. If so, should these activities be delivered by government, regulators or a different organisation?

14. How can we avoid overlapping, duplicative or contradictory guidance on AI issued by different regulators?

Monitoring and evaluation of the framework

15. Do you agree with our overall approach to monitoring and evaluation?

16. What is the best way to measure the impact of our framework?

17. Do you agree that our approach strikes the right balance between supporting AI innovation; addressing known, prioritised risks; and future-proofing the AI regulation framework?

18. Do you agree that regulators are best placed to apply the principles and government is best placed to provide oversight and deliver central functions?

Regulator capabilities

19. As a regulator, what support would you need in order to apply the principles in a proportionate and pro-innovation way?

20. Do you agree that a pooled team of AI experts would be the most effective way to address capability gaps and help regulators apply the principles?

Tools for trustworthy AI

21. Which non-regulatory tools for trustworthy AI would most help organisations to embed the AI regulation principles into existing business processes?

Final thoughts

22. Do you have any other thoughts on our overall approach? Please include any missed opportunities, flaws, and gaps in our framework.

Legal responsibility for AI

L1. What challenges might arise when regulators apply the principles across different AI applications and systems? How could we address these challenges through our proposed AI regulatory framework?

L2.i. Do you agree that the implementation of our principles through existing legal frameworks will fairly and effectively allocate legal responsibility for AI across the life cycle?

L.2.ii. How could it be improved, if at all?

L3. If you are a business that develops, uses, or sells AI, how do you currently manage AI risk including through the wider supply chain? How could government support effective AI-related risk management?

Foundation models and the regulatory framework

F1. What specific challenges will foundation models such as large language models (LLMs) or open-source models pose for regulators trying to determine legal responsibility for AI outcomes?

F2. Do you agree that measuring compute provides a potential tool that could be considered as part of the governance of foundation models?

F3. Are there other approaches to governing foundation models that would be more effective?

AI sandboxes and testbeds

S1. Which of the sandbox models described in section 3.3.4 would be most likely to support innovation?

S2. What could government do to maximise the benefit of sandboxes to AI innovators

S3. What could government do to facilitate participation in an AI regulatory sandbox?

S4. Which industry sectors or classes of product would most benefit from an AI sandbox?

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Portfolio: Written handshake / introduction

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Example content for 'written handshake' from Co-Pilot

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Portfolio content (example based on BA L3)

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Portfolio content (example based on BA L3)

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Education for sustainable development in action

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Education for sustainable development for 2030 toolbox

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Selected resources to develop ESD activities

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The problem

The education sector is key to a more sustainable future - both in terms of the operations and estates of education settings, and ensuring we are preparing learners with awareness and green skills. Many settings have already made a start on their sustainability journeys. But research tells us they struggle to progress due to lack of time, resource, and sustainability expertise.

We know there are lots of great resources out there to help education settings overcome these challenges. However, educators tell us it can be overwhelming just deciding on next steps, and they don't have the time or know-how to find the right support for their setting.

What we're doing to help

Our aim is to promote the good work that has already been done in creating sustainability support for education settings. We will help settings find resources (such as guidance, support services, toolkits) that suit where they are on their sustainability journey, and guide them to take their next steps.

We will work closely with resource providers over the coming months to add resources that suit different settings across different aspects of sustainability.

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Sustainability Support for Education will help you find ideas and resources that are relevant to you and your setting.

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Video that explains the Triple Bottom Line

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What Is the Triple Bottom Line? | Business: Explained

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Education for Sustainable Development in Higher Education

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Education for Sustainable Development provides formal and informal learning opportunities which enable all students to develop the knowledge, skills, attitudes and values required to contribute to a sustainable future.

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Even before the COVID-19 pandemic, the world was already off-track to achieve its education targets. If no additional measures are taken, only one in six countries will meet SDG4 and achieve universal access to quality education by 2030. An estimated 84 million children and young people will still be out of school and an estimated 300 million student will still not have the basic numeracy and literacy skills they need to succeed in life. To deliver SDG4, education systems must be re-imagined, and education financing must become a priority national investment.

Target 4.1: Between 2015 and 2021, the school completion rate increased from 85% to 87% in primary, from 74% to 77% in lower secondary and from 54% in 2015 to 58% in upper secondary education. Even before the onset of COVID19, these rates had slowed down relative to progress in 2010-15. Looking closely at reading levels at the end of primary school, for which trend data cover 34% of the world's children, the analysis shows that global learning levels showed no progress between 2015-2019. Furthermore, learning losses due to COVID-related school closures have been documented in 4 out of 5 of the 104 countries that have carried out such studies.

Target 4.2: Participation rate in organized learning one year before the official primary entry age has stagnated at around 75% since 2015, still far from the target of ensuring that all girls and boys have access to quality pre-primary education by 2030.

Target 4.3: Among 131 countries with data from 2017 onwards, on average approximately one in six youth and adults aged 15-64 recently participated in formal or non-formal education and training. Participation is substantially higher among youth aged 15-24 (40%-50%), compared to those aged 25-55 (only 5% for most regions).

Target 4.a: Basic school infrastructure is far from universal. In 2020, approximately a quarter of primary schools globally do not have access to basic services such as electricity, drinking water and basic sanitation facilities. For other facilities such as computer facilities and the provision of disability adapted infrastructure, figures are substantially lower, with around 50% of primary schools with access.

Target 4.c: Globally, in 2020, over 14% of teachers are still not qualified according to national norms, with little improvement since 2015.

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Goal 4 | Department of Economic and Social Affairs

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Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

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UK Gov: Sustainability and climate change strategy for education

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UK Gov: Sustainability and climate change strategy for education

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Scope and purpose

This strategy applies to:

the Department for Education (DfE), its agencies and public bodies

the education and children's services systems in England - including:

early years

schools (and independent schools where applicable)

further education

higher education

children's social care

DfE has an important role to play in all aspects of sustainability. But the area in which we have the most work to do is reducing our environmental footprint, particularly in the drive to achieve net zero.

While the policies set out here are focussed on the environmental aspect of sustainability, this is done with consideration for how those policies will interact with the social and economic aspects of sustainability.

The UK government and the devolved governments (Northern Ireland, Scotland and Wales) are committed to climate action, and we will work together in our international action. The targets and strategic aims set out relate to England.

It brings together short, medium and longer-term actions that will enable us to make progress towards achieving our 4 strategic aims and overarching vision. It is a strategy to 2030.

Action will be reviewed and updated annually to respond to new opportunities, scientific updates, evidence and data.

This strategy has been informed by:

Foreword
Secretary of State for Education, the Rt Hon Nadhim Zahawi MP
Since spring of 2020, the British education system faced its most challenging period for generations: sustaining learning through a pandemic. The whole sector met this challenge with great resilience. Now, the UK must show the rest of the world how our education system can transition out of the pandemic and, in doing so, face the even greater environmental challenge ahead of us, by Building Back Greener.

We have promised to level up the country, to boost economic growth, encourage innovation, create good jobs and enhance educational attainment so all may share equally in our nation's success. To achieve this in a world influenced by the effects of climate change, we must level up within a context of sustainability, giving all children, young people and adults the knowledge and skills to thrive in the green economy and to help restore nature.

The challenge of climate change is formidable. For children and young people to meet it with determination, and not with despair, we must offer them not just truth, but also hope. Learners need to know the truth about climate change - through knowledge-rich education. They must also be given the hope that they can be agents of change, through hands-on activity and, as they progress, through guidance and programmes allowing them to pursue a green career pathway in their chosen field.

I would like to thank the large number of people that have worked with us in developing this strategy. The feedback from young people, educators, academics, leaders, governing bodies and experts has been invaluable in shaping this first step on our journey. By continuing to work together in the delivery of the strategy we will seek further opportunities to build on the action set out and update on shared progress annually.

I am confident that together we will support net zero through skills and decarbonisation; play a significant part in nature's recovery; build an education system resilient to climate change; and, provide the truth and hope that young people need to flourish in our changing world.

Context

The UK requires the education sector to play its role in positively responding to climate change and inspiring action on an international stage. This strategy sets out ambitious activity to respond to recommendations for education from the:

It supports the delivery of the government's 25 Year Environment Plan and Net Zero Strategy. It includes how we will meet legislative requirements and work in the context of:

the United Nations' 17 Sustainable Development Goals and UNESCO's 'Education for Sustainable Development (ESD) for 2030' which set out the key role of education in the successful achievement of the goals

the Paris Agreement and Glasgow Climate Pact, which are important steps towards keeping the goal of 1.5 degrees Celsius alive

Action for Climate Empowerment (ACE) which includes taking action to empower society to engage in education and training

the United Nations Convention on the Rights of a Child (UNCRC) ensures the needs and views of children are taken into account in the development of climate change policies

the UK Climate Change Act (2008) and National Adaptation Programme (NAP), which include action for education in adapting to climate change and require progress reporting against priority risks

UK government legislation to meet net zero by 2050 - the UK is the first country to set legally binding carbon budgets, placing a restriction on the total amount of greenhouse gases the UK can emit over a 5-year period. In the latest, Carbon Budget 6, the UK legislated to reduce emissions by 78% by 2035 compared to 1990 levels

the Environment Act (2021) reflects the need to tackle climate change through nature-based solutions - following the close of the targets consultation in May 2022, government will set statutory targets for air quality, biodiversity, water, resource efficiency and waste reduction, and a new target to reverse the decline in species abundance by the end of 2030, while the act also strengthens the biodiversity duty on public authorities, including the Department for Education

the cross-government 25 Year Environment Plan, which commits to encouraging children to be close to nature both in and out of school

The challenge and the opportunity

Children and young people are worried about climate change and want to know more about:

the impact it is having now

how it will impact their future lives

DfE and the education sector have a joint responsibility for preparing children and young people for the challenges and opportunities they will face - with the appropriate knowledge, skills and pastoral care.

Education

Through education we have the privilege to be able to engage directly with children and young people who:

Through their learned and lived experiences from early years to further and higher education, we will provide opportunities to develop a broad knowledge and understanding of the importance of nature, sustainability and the causes and impact of climate change and to translate this knowledge into positive action and solutions.

In the UK, there are more than 16 million children, young people and adults in education. The enthusiasm of youth can inspire the whole of society to work together at the start of this crucial decade for the planet.

Green skills and jobs

Green jobs will not be niche. We anticipate that sustainability and climate change will touch every career.

The UK government's Net Zero Strategy will support the transition to net zero and 190,000 jobs by the middle of the 2020s and up to 440,000 jobs in 2030. The increased investment and legislation for nature's recovery driven by the Environment Act will also create many green jobs in the nature sector. Our programmes will provide the opportunity for people of all ages to train, retrain or upskill to go into green careers.

Learning from and connecting with nature

The Economics of Biodiversity: The Dasgupta Review states that 'connection with nature declines in childhood to an overall low in the mid-teens. Creating an environment from an early age where we are able to connect to nature is essential for self-enforcement in protecting and valuing nature'.

We will increase opportunities for all children and young people to:

spend time in nature and learn more about it

become actively involved in the improvement of their local environment

We know that regular contact with green spaces can have a beneficial impact on children's physical and mental health. However, access to green space is not equal and we must do more to ensure that all children have opportunities to benefit from access to green space and build connections with nature.

Buildings

Climate adaptation and decarbonisation activity in nurseries, schools, colleges, universities and care buildings can provide powerful learning opportunities. Young people can:

Seeing sustainability brought to life in the buildings around them will allow children and young people to gain experiences which will enhance and contextualise their learning.

Leading by example: cutting energy bills and carbon emissions in the public and higher education sectors shows that schools and universities represent 36% of total UK public sector building emissions.

Costs are also significant and rising. Data sources and interpretation - schools financial benchmarking shows that in 2019 schools alone were spending around £630m per annum on energy and we expect today's figure to be far greater. To reduce energy usage and achieve legal targets for carbon emissions we need to:

get a better understanding of the scale of the problem across the sectors through standardised reporting

drive action based on evidence

Adapting existing buildings and designing new ones to respond to climate change and reduce emissions will undoubtedly be a significant challenge.

By developing and delivering evidence-based solutions for decarbonisation and climate resilience we will:

reduce energy demand

adapt to climate risks

drive innovation in construction

act as a catalyst for green jobs

deliver savings

We will also drive a wide range of additional benefits that will contribute to a vibrant and resilient education community, including:

International

Through learning from others and sharing our own expertise internationally, we will be more effective and innovative. Sustainability and climate change are global problems which require collective efforts and evidence-based solutions.

Vision and aims

Vision: the United Kingdom is the world-leading education sector in sustainability and climate change by 2030.

In England, we will achieve this through the following strategic aims:

1. Excellence in education and skills for a changing world: preparing all young people for a world impacted by climate change through learning and practical experience.

2. Net zero: reducing direct and indirect emissions from education and care buildings, driving innovation to meet legislative targets and providing opportunities for children and young people to engage practically in the transition to net zero.

3. Resilience to climate change: adapting our education and care buildings and system to prepare for the effects of climate change.

4. A better environment for future generations: enhancing biodiversity, improving air quality and increasing access to, and connection with, nature in and around education and care settings.

Guiding principles

Partnership and collaboration

Success depends on DfE and stakeholders from across the sectors working together.

We know there are many organisations working to improve sustainability and mitigate against climate change, so we will seek opportunities to:

work with others who share our objectives

extend and amplify existing good practice and initiatives

We will also facilitate partnership across the sectors to maximise the use of resources, expertise and ideas to achieve more effective outcomes.

Evidence and insight

Evidence will be at the heart of our activity. By 2023 we will develop and publish a framework to evaluate the impacts of the actions set out within the strategy.

We will also work with the sectors to develop reporting frameworks and processes to ensure strategic decisions can be taken at every level. While mandatory reporting is not currently set out within the strategy, this decision will be kept under regular review. The decision to mandate is subject to uptake and legislative reporting requirements in line with government's Net Zero Strategy.

We will work together to set science-based targets from 2025, ensuring we play our part in reducing public-sector emissions against a 2017 baseline by:

50% by the end of Carbon Budget 5 (2032)

75% by the end of Carbon Budget 6 (2037)

There are many opportunities to improve sustainability and impact climate change. Ensuring we learn before we leap, we will work across the sector to research, test and pilot with:

As our evidence base for what works grows, we will prioritise funding where it will have greatest impact.

Leadership and support

Sector representatives have called on DfE to provide greater leadership in sustainability. We will work across government and intervene where we add value (for example through sector-wide data gathering, reporting, funding and guidance). Wherever possible we will facilitate autonomy and foster innovation.

Sector leadership and institution-level accountability for sustainability will be key for success. We will continually seek opportunities to share best practice and provide support for those leading at a local level.

A whole system approach

Together we will make the greatest impact. We will adopt a systems-based approach and ensure our initiatives are delivered via action areas to deliver outcomes for the education system, rather than focussing on specific age or sectors.

By taking a holistic view of education (from formal and extracurricular learning opportunities, the physical buildings and surroundings, to the way in which they operate) we will positively improve the environment. At the same time we will influence and inspire, not only those that use the settings, but the communities around them.

Initiatives to drive the strategy

We have carried out work with sector representatives and experts to develop initiatives that bring together activity to drive the strategic aims:

National Education Nature Park

By considering the whole physical education estate as a virtual National Education Nature Park, we have a unique opportunity to:

The National Education Nature Park will:

This will help connect and amplify the excellent work already happening in this area through many national and local stakeholders and community groups.

As their work starts to have an impact, the young participants will upload their progress on the park's digital mapping services. They will be able to:

see how the park is 'growing', while increasing their knowledge of species

develop important skills, such as biodiversity mapping, data collection and analysis

The nature park's online hub will enable the sharing of best practice across the education estate. With time, it will go beyond biodiversity to show impact and climate resilience, including flood, overheating and air quality status of the estate.

A number of universities will support the launch of the National Education Nature Park by:

Climate Leaders Award

A Climate Leaders Award will complement classroom learning and allow us to celebrate and recognise education providers, children and young people for:

developing their connection with nature

making a real contribution to establishing a sustainable future for us all

The award will provide a structured route through existing awards in this area, such as the John Muir Award, Duke of Edinburgh's Award, Junior Forester Award and others.

Participation will enable children and young people to acquire credits towards the prestigious Climate Leaders Award. This will be recognised and valued as supporting progression to employment and further study.

In the Children's People and Nature Survey for England: Summer holidays 2021, more than 8 in 10 children and young people (85%) agreed that being in nature made them very happy. However, the same study showed that:

children spend less time outdoors as they get older

feelings of connection to nature decline with age

We want all young people to feel connected to their local environment and see improvements in biodiversity. In the design and implementation of the park and award we will take steps to drive participation among more disadvantaged children and young people. We will also ensure all children and young people, whether they live in an urban area or rural one, have opportunities to feel empowered through practical positive action.

The National Education Nature Park and Climate Leaders Award:

are being designed and developed with young people, sector representatives and stakeholder organisations

will build on the learning and experience of the Year of Green Action

They will feed in ideas for making the initiatives as inclusive, impactful, and engaging as possible and will also consider the best names for the programmes. The park and award will be launched in autumn 2022.

Sustainability leadership

Throughout the development of this strategy, we have received feedback from the sector that they have seen the greatest impact and success in developing and delivering a whole-setting approach to sustainability and climate change when there is co-ordination and leadership of activity at a local level - with the support of senior leadership teams and governors.

Through our engagement to date, we have been consistently told that to do this best, they need more support to:

We recognise the impact of the pandemic and the priority need for education catch up. We know that for every setting, the ability, or the way in which they are able to engage with sustainability and climate change may differ. While our end goal is for all settings to have a sustainability lead owning a climate action plan (to include curricular and extra-curricular activity, procurement, adaptation and decarbonisation plans), we are keen to:

develop a facilitative approach to sustainability leadership

test where support makes the most impact

The National Climate Education Action Plan has already brought together 16 organisations and universities with STEM Learning, aiming to encourage their staff to become Climate Ambassadors to support schools and further education institutions. We will work with them to test an approach that includes support in:

climate education

accessing public and private funding

engaging in the National Education Nature Park and Climate Leaders Award

reporting emissions

developing and delivering Climate Action Plans for individual settings

As part of this approach, we will include the provision of carbon literacy training for all sustainability leads in every nursery, school and college by 2025. We will encourage a joined-up approach to leadership which brings together children, young people and governors.

Action area 1: Climate education

We know that young people are eager to:

create a greener, sustainable world

tackle both the causes and impact of climate change

We will empower all young people to be global citizens, through a:

better understanding of climate change

greater connection to nature

Practical opportunities to participate in activities to increase climate resilience, reduce carbon impact and enhance biodiversity will enable children and young people to translate knowledge into positive action to improve their local communities, their country and the planet.

1. Learning about the natural environment

Building on a foundation of fundamental numeracy, literacy and broad academic knowledge, all children learn about:

From birth to 5 years old, the early years foundation stage (EYFS) framework ensures that all children develop an understanding of the world and the natural environment.

As they progress through primary and secondary school, children and young people continue to build on this knowledge through science, geography and citizenship programmes within the national curriculum. Existing GCSEs such as design and technology, food preparation and nutrition, and economics contain opportunities for students to be taught about the environmental and sustainability context of the processes and principles underlying these subjects.

By 2025 we will aim to introduce a natural history GCSE, giving young people a further opportunity to engage with and develop a deeper knowledge and understanding of the natural world. In studying this GCSE, young people will explore organisms and environments in more depth, gain knowledge and practical experience of fieldwork and develop a greater understanding of conservation.

An environmental science A level is already available for those that have a keen interest in the sustainability of our planet and wish to build on their scientific knowledge of the interconnection of human activities with natural systems.

From 2022, to assess the impact of our action, we will introduce an annual climate literacy survey to benchmark progress in improving the climate knowledge of school leavers.

For those that continue their studies in further and higher education, there are many excellent opportunities to gain a more in-depth knowledge into sustainability and climate change. Many further and higher education providers are already taking steps to embed the relevant teaching of sustainability and climate change across the full range of their courses.

We will continue to identify appropriate opportunities to align climate education with the Education for Sustainable Development (ESD for 2030) framework.

2. Support for teaching

World class teaching will ensure all children and young people get the best possible climate education. The schools white paper sets out how we will provide an excellent teacher for every child, by giving every teacher and school leader access to world class training and development opportunities, including the first-ever national professional qualification (NPQ) for early years professionals.

Through our engagement with the teachers and representative bodies, we have heard that more support in teaching about climate change and in navigating the many different resources available is also needed. Therefore, recognising the importance of building confidence as well as capability, we will provide additional support to teachers of all levels.

From 2022 we will:

include climate change and sustainability in science teachers' continuing professional development ( CPD) to ensure all young people receive high-quality teaching on the scientific facts about climate change and environmental degradation

include content on sustainability when DfE is tendering for new CPD, (where it is relevant to the subject area)

deliver pilots through the National Education Nature Park, which will test an approach for sharing university climate expertise and learning opportunities with colleges, schools and nurseries

share best practice, demonstrating how sustainability and climate change has been incorporated into teaching (and how it has enriched the broader curriculum, where it is relevant) in early years settings, schools, colleges and universities, so teachers and leaders can consider how best to integrate within their own settings

consider where further steps could be taken to support the teaching of sustainability in relevant subjects, for example, the circular economy in design and technology, sustainable chemistry in the sciences, and through the learning initiatives being taken forward in the Government Food Strategy

continue to work with higher education to identify opportunities to work together to further enhance best practice in teacher training and the teaching of sustainability within university courses

promote and share relevant teaching resources from other government departments, including the Department for Environment, Food and Rural Affairs ( DEFRA), the Waste and Resources Action Programme ( WRAP) and the Environment Agency

By 2023 we will:

develop a Primary Science Model Curriculum, to include an emphasis on nature to ensure all children understand the world around them

develop an occupational standard for further education teaching which explicitly requires all new teachers to integrate sustainability into their teaching, through modelling sustainable practices and promoting sustainable development principles in relation to their subject specialism

support the National Climate Education Action Plan in setting up an independent expert body (including members such as the Royal Meteorological Society, STEM Learning, Association of Science Teaching) for the validation and creation of climate education resources that support the delivery of the national curriculum

provide free access to high-quality curriculum resources through the National Education Nature Park online hub, so that teachers in all settings and subjects can confidently choose those that will support the teaching of sustainability and climate change

this will initially work in tandem while we build on the success of Oak National Academy's work in the pandemic and establish a new arms-length national curriculum body which will work with thousands of teachers to co-design, create and continually improve packages of optional, free, adaptable digital curriculum resources and video lessons that are effectively sequenced to help teachers deliver an evidence-based, high-quality curriculum

provide the opportunity for all staff (teaching, leadership and support) to build their understanding of climate change and sustainability by receiving shared carbon literacy training through their sustainability leads within their setting (more information about sustainability leadership is set out in the Operations and Supply Chains Action Area)

3. Learning in the natural environment

Education settings provide a wealth of learning opportunities, practical activities and clubs which allow children and young people to bring their learning to life. Children and young people may:

take part in eco-clubs or vegetable growing

be exposed to sustainable food choices, recycling, adaptation projects or weather and energy monitoring

On top of the learning benefits, these activities can aid pastoral work in all educational settings. The physical and mental health benefits of time spent in nature can form part of targeted support to:

improve engagement and attainment, including as part of wider packages of support for pupils with SEND

give young people a sense of agency where anxiety stems from climate concerns

The National Education Nature Park and Climate Leaders Award will build on this excellent activity, ensuring all children and young people have opportunities to get practical experience and turn their knowledge into positive action. We will design these initiatives with inclusivity at their heart. We are committed to enabling those from disadvantaged backgrounds to access these opportunities.

Retrofitted educational buildings and new-build net-zero settings will also provide various learning opportunities through their design, how they work and their environmental impact. They will foster environmental knowledge that can be applied outside of the educational setting.

In 2022 we will:

explore opportunities to increase access to the outdoors in DfE's successful Holiday Activities and Food (HAF) programme

ensure that children attending the HAF programme participate in activities that improve their knowledge and awareness of affordable, healthy eating and sustainable practice - for example, through:

taking part in activities such as food preparation

cooking, discussing food and food sources

growing their own fruit and vegetables

build on our investment into the Children and Nature Programme, by engaging with DEFRA on further research into how outdoor learning can be delivered for the greatest impact, with a view to exploring opportunities to support education settings to deliver quality outdoor education

link up across government to identify new opportunities and strengthen action as part of the levelling up agenda - for example, with the Department for Levelling Up, Housing and Communities on climate education, and with the Department for Digital, Culture, Media and Sport (DCMS) on the National Youth Guarantee to increase access to out-of-school activities for all children

provide co-curricular opportunities for young people to experience and learn about nature through the National Education Nature Park, which will drive a greater knowledge and understanding of the importance of native species and develop young people's data and analytical skills

build on learning from the Children and Nature Programme, encouraging schools to join up with local delivery partners to develop effective school-specific options and build links with local communities

provide educational activities so that leaders, teachers, children and young people will learn about energy efficiency, the circular economy, climate resilience and green careers as part of educational building, maintenance and procurement projects, such as:

low-carbon boiler replacements

smart meter installation

energy monitoring pilots

sustainable drainage systems

Political impartiality

Teaching about climate change, and the scientific facts and evidence behind this, does not constitute teaching about a political issue and schools do not need to present misinformation or unsubstantiated claims to provide balance.

However, in climate education there is relevant political and scientific debate about the best ways that climate change can be addressed - there are different views and opinions, and different solutions. Debates on political and policy change need to be grounded in wider citizenship education on democracy and democratic values and topics should be handled in line with schools' legal duties on political impartiality

Action area 2: Green skills and careers

It is critical young people and adults have the green skills that will allow them to build careers and participate as Britain leads the world into the Green Industrial Revolution and strives for nature's recovery.

We will harness young people's passion and interest in climate change and sustainability to enable them to have the knowledge and skills (in STEM and other key subjects) required for green jobs. Through our education and skills system, we will seek to inspire young people to choose career paths that support the:

We will continue to support adults already in work to retrain and reskill in line with the needs of the green economy.

1. Net Zero Strategy

The government's Net Zero Strategy sets out in detail how our skills reforms will support more people into green jobs and help grow future talent pipelines. This includes:

aligning apprenticeships to net-zero objectives through the Institute for Apprenticeships and Technical Education's (IfATE) Green Apprenticeships Advisory Panel

continuing the roll-out of T-Levels to support young people into green careers

driving STEM provision through our growing network of Institutes of Technologies (IoTs)

expanding Skills Bootcamps so that adults are able to upskill and retrain in key green sectors

Through the Skills and Post-16 Education Bill, we are legislating to ensure employer leadership of Local Skills Improvement Plans (LSIPs) have regard to skills needed to help deliver on our net-zero target, adaptation to climate change, and other environmental goals.

Building on the work of the Green Jobs Taskforce through a Green Jobs Delivery Group, government will bring together representatives from industry, the skills sector and other key stakeholders to develop data-driven action to support the delivery of plans for green jobs and skills. This will ensure that we are offering young people and workers the opportunity to develop the skills needed to deliver the low-carbon transition.

Since the publication of the Net Zero Strategy we have:

set out investment of £3.8bn in further education and skills, to ensure people can access high-quality training and education that leads to good jobs, addresses skills gaps, boosts productivity and supports levelling up - this includes funding for programmes to support green skills crucial to the net-zero transition

committed to expanding Skills Bootcamps to provide more opportunities for adults to gain new skills, prioritising green skills in areas such as heat pumps, zero emission vehicles, and carbon capture

widened the eligibility criteria of our Free Courses for Jobs offer - from April 2022, any adult in England earning under the National Living Wage annually, or unemployed, will be able to access these qualifications for free, including qualifications linked to green sectors such as agriculture, building and construction, engineering, environmental conservation, horticulture and forestry and science

launched a pilot of our new General Further Education College Accountability Agreements that will ensure colleges strategically plan provision to meet skills needs at both local and national levels, including those in the green sector

established 8 LSIP trailblazers, where employer representative bodies have been working closely with providers and key local stakeholders to develop tailored plans to help shape technical skills provision to better meet local labour market needs, with multiple trailblazers considering the skills needed to support green growth in their local economy

started delivery of our £65 million Strategic Development Fund pilots to identify employers' skills needs, design provision, and build the capacity of local further education providers to deliver them so that, where local areas identify skills needs, providers can use this funding to, among other things:

purchase equipment

train their staff

bring in industry expertise to provide training

deliver new provision

2. Additional support for green jobs and skills

In addition to the extensive skills reforms set out in the Net Zero Strategy we will also increase the opportunities for young people and adults to engage in wider green skills and jobs needed to deliver the government's 25-Year Environment Plan.

We will support the further and higher education sectors as they deliver programmes teaching the skills of the future and nurturing future leaders.

We will continually seek to improve diversity in the take-up of STEM subjects at all levels, ensuring that everyone has the opportunity to pursue a rewarding career in a STEM occupation:

by September 2023, a T Level in agriculture, land management and production will be available - this will support the increased focus of the UK agriculture sector on more environmental and sustainable practices and provide a further opportunity and pathway for young people looking for careers in agriculture and horticulture

we will engage employers, professional, statutory and regulatory bodies (PSRBs), further and higher education sector representative groups and students in developing opportunities to forge further links between course content and the skills needed for green careers of all kinds

Green Skills Bootcamps are available in environmental areas such as agriculture technology, and Wave 3 Skills Bootcamps will seek to prioritise further green sectors, including those specific to the environment such as nature restoration, waste management and recycling, woodland management and arboriculture.

the Institute for Apprenticeships and Technical Education (IfATE) has convened a Green Apprenticeships Advisory Panel (GAAP) to:

work with employers to align apprenticeships to net-zero objectives

endorse existing apprenticeships which support green career pathways specific to the environmental sector, such as arborist, forest operative, agriculture or horticulture professional advisor and ecologist

map existing apprenticeship standards against green occupations

identify opportunities to create new standards in areas including agri-tech and renewable energy

investment of up to £75 million in a National Scholarship Scheme will support high achieving disadvantaged students to reach their full potential while studying in higher education, including degree courses or apprenticeships that can lead to green careers

our network of Institutes of Technology (IoTs) across England are utilising their state-of-the-art facilities to:

offer training in green skills, such as in the built environment and agri-tech sectors

support increased participation from under-represented groups, including women, helping to grow the pipeline of individuals with STEM skills needed for green jobs

invest £120 million in the second wave of IoTs, to be set up by 2022

by 2025, as part of the pathway towards the Lifelong Loan Entitlement, we will trial short-course provision at levels 4-6 to support in-work adults to upskill or retrain, enabling learners to build flexibly towards a full qualification in subjects crucial for net zero, including STEM and digital innovation

by 2023, we will co-host an International Green Skills Conference with the higher and further education sectors to exhibit the best of UK green skills and education opportunities at further and higher education levels

we will link the Climate Leaders Award to skills required by employers and explore how participation in the award can act as a progression route to further study

by 2030, significant numbers of young people will have graduated from the Climate Leaders Award with the skills needed to enter the green economy

by 2030, we anticipate that participation in the National Education Nature Park, and increased opportunities to connect with nature, will increase the number of young people that become data scientists, ecologists and biologists, which are needed for nature's recovery

our specifications for new buildings set out low-carbon, climate-resilient standards - such as the installation of heat pumps for energy efficiency - while, as one of the largest construction buyers in the nation, these specifications will drive the market and assist in driving the development of technical fitting skill

3. Support and guidance for green careers

We are actively supporting young people and adults to understand the training and careers opportunities available to them and we will support existing organisations in their endeavours to promote green careers. This includes:

supporting schools and colleges to embed best practice in the delivery of careers information, advice and guidance, so young people are aware of the full range of training and careers available to them and have access to a broad range of employers and workplaces, including those in the green sectors - this will be delivered through the national roll-out of Career Hubs, Career Leader training, and the Enterprise Adviser Network

as part of wider improvements to the National Careers Service we will ensure the provision of relevant and accessible information on green career pathways, which will bring together all the learning and careers routes available to aid the development of a single source of government-assured career information

our educational building maintenance and procurement projects will provide opportunities for students to gain careers guidance, while installation of low-carbon boiler replacements, smart meters and energy monitoring or sustainable drainage systems, will be accompanied with assembly information and interactive class exercises allowing students to see and learn about a wide range of green careers and skills in action

Action area 3: Education estate and digital infrastructure

A green, sustainable education estate that is resilient to the impacts of climate change will normalise and inspire young people to live sustainable lives, with impact felt widely in their families and communities. By improving the physical environment in and around education settings, we can impact positively on both the physical and mental wellbeing of children and young people. We will act on both the challenges and opportunities the vast education estate presents.

There is still a lot of evidence to gather on new technologies and innovative approaches to sustainable building design, retrofit, ICT, building management and the surrounding environment. Our focus until 2025 will be piloting - gathering evidence and sharing research to learn from our experience. This work will build on and align with existing guidance currently available to help public sector organisations achieve net zero, for example, the Modern Energy Partners tools. From 2025 onwards, we will accelerate change once we understand the best value for money approach. Our approach can be summarised as innovate, test and invest.

1. New builds and new blocks

All new school buildings delivered by DfE (not already contracted) will be net zero in operation. They will be designed for a 2oC rise in average global temperatures and future-proofed for a 4oC rise, to adapt to the risks of climate change, including increased flooding and higher indoor temperatures.

Secure children's homes delivered by DfE will also be built to our net-zero standard.

In November 2021 we published our delivery framework for employer's requirements for school projects Part A and Part B and made it freely available so that all new schools and colleges can meet the same low-carbon, climate-resilient standards as centrally delivered projects.

We have uplifted basic need grant-funding rates to help local authorities deliver school capital projects to these standards. When planning the use of basic need funding allocations, local authorities will consider environmental sustainability, carbon reduction and energy efficiency to develop solutions for projects that are in line with government targets and objectives, regardless of their chosen delivery route.

From now on bids into our Further Education Capital Transformation Programme will also be assessed to determine if the new works will be net zero in operation.

We know many higher education institutions are already demonstrating innovation in this space and are going beyond these standards. We will look to the higher education sector to inspire and drive greater ambition through their net-zero buildings and campuses.

So that our education building standards remain world-leading, we will:

continue targeted and specific monitoring of education buildings to better understand their use and energy consumption

continually improve our technical standards and building specifications

This will include the use and assessment of nature-based classroom design to maximise access to the outdoors and opportunities for outdoor learning, so our education buildings positively impact on physical and mental wellbeing.

Sustainable ICT solutions will be integrated into new-build schools as standard, via sustainable procurement, design, implementation and management.

The implementation of ultra-low carbon education buildings will be accelerated. By 2025 at least 4 schools and one college will have been built via the Gen Zero Platform that we demonstrated at COP26.

We will also accelerate our Gen Zero programme and other pilots so, over time, all centrally delivered new-build projects are built using ultra-low carbon solutions.

2. Existing estate

Improved collation and use of data on energy usage, water, heat and biodiversity will allow individual settings, and DfE, to make evidence-based decisions on where to act and invest to make the greatest impact. Smart meters and energy management systems can reduce bills and usage. They also provide learning opportunities for data analysis and understanding the impact of human behaviour. We will:

continue to work with the Department for Business, Energy and Industrial Strategy ( BEIS) and water companies to trial the delivery of smart meters in schools and encourage the uptake across other settings

in 2022, work with the education and children's services sectors and energy companies to explore sharing smart meter data to avoid additional reporting requirements, and this data would also be used by DfE to help advise settings how they can reduce their energy bills and improve their energy efficiency

during 2022 and 2023, work with BEIS and Energy Sparks to trial the delivery of Energy Management Systems in schools which will provide schools with real-time information about their energy usage, presented within a user-friendly online portal

by 2025, have supported education settings to put in place Climate Action Plans, facilitating the use of setting-level data to inform action - these will increase carbon literacy and inform government on the implementation of decarbonisation solutions and nature-based solutions to alleviate flood risk, protect against increased heat, and improve air quality

A strategic approach to piloting new building technology will also be launched in order to support the future retrofit of the education estate and act as catalyst to the construction sector for implementing new technology. Our building technology pilots will support action to adapt the existing estate to protect against the current and future effects of climate change. They will also provide evidence for the efficient decarbonisation of the estate to mitigate the causes of climate change.

We will continue to seek opportunities for research, including developing innovative approaches for the delivery and use of digital infrastructure. Where opportunities arise, this will be tested in schools by retrofitting ICT to deliver energy savings.

We will also exploit existing research -higher education is already driving world leading building technology - and innovative solutions will be shared across the wider education sector

In 2022:

we will work with BEIS to help education settings access the Public Sector Decarbonisation Scheme to support them in decarbonising their estates and, as part of that work, we are looking at how we can better align our application processes and funding windows

By 2023:

all bids for capital funding for further education and higher education will need to consider environmental impact, carbon reduction and adaptation measures, and align with the government's targets and objectives

we will share best practice and advice to help education settings in deciding where to invest money to reduce carbon emissions, improve sustainability and resilience, and share options and case studies for the retrofit of existing settings by building type

we will also develop the existing Good Estate Management for Schools guidance with updated tips and good practice on the sustainable management of the school estate, covering topics such as energy and water efficiency and addressing issues including ventilation and leaks

3. Resilience, adaptation, access to Nature and environment conditions

The 3 highest priority risk areas identified in the Climate Change Committee Independent Assessment of UK Climate Risk were:

To meet these challenges and those outlined in the Third Climate Change Risk Assessment (the statutory cycle set out in the Climate Change Act 2008, specifies the next key milestone in the statutory cycle as the third National Adaptation Programme ( NAP3) in 2023) we have a duty to demonstrate:

a clear set of objectives for adaptation

policies

programmes

investments with clear timelines

measurable metrics

progress indicators

We know that nearly half of schools (10,710) are at risk of flooding, which is expected to increase to at least 13,662 by the 2050s, or 16,394 in the worst-case scenario. While overheating in education buildings is a significant threat, we need to continue to work with other government departments to get a greater understanding of the risk.

A formal Memorandum of Understanding between DfE and the Environment Agency ( EA) will ensure that our response to risk aligns with the objectives and measures set out in the National Flood and Coastal Erosion Risk Management Strategy and is based on the latest environmental data, advice and intelligence.

We know there is hope if efforts are focused in the right way. The latest International Panel on Climate Change report outlines the power of nature against the worst effects of climate change. We will use the National Education Nature Park to champion the case for nature.

Through the National Education Nature Park we will provide opportunities and advice for education settings to initially improve their biodiversity and also, with time, their flood resilience and air quality. For example, we will work with EA and DEFRA to produce guidance for schools that explains how to improve their flood resilience and access free trees to improve shading and biodiversity of their grounds.

We will accelerate pilots to investigate:

We will also empower universities to lead the way in demonstrating models for resilient, biodiverse campuses. We will share best practice via the National Education Nature Park online hub.

By 2023, we will:

work with partners to explore how funding may support nurseries, schools, colleges and universities in driving on the ground innovation to link separate parts of the National Education Nature Park, improving the biodiversity, air quality, shading and flood resistance along the routes between settings

continue evaluating the UK's first 'Biophilic' primary school, which will provide evidence of the impact of a greater connection to nature within the built environment, on the health and wellbeing of children and young people

pilot the use of smart air and environmental quality monitors in schools to understand the practical implications, benefits and drawbacks of using smart monitoring devices with pilots providing a rich dataset on environmental performance, which can be used to help feed into school building design and understand where energy efficiencies can be achieved

support DEFRA's Clean Air Strategy which aims to diminish the highest concentration of exposure to fine particle matter (PM2.5) to help tackle air pollution and reduce emissions affecting education settings

provide guidance to all education settings for practical ways to reduce indoor and outdoor air pollution, encouraging schools and partners to expand anti-idling zones to reduce the impact of the school commute

support the Department for Transport in delivering initiatives to increase active and safe travel to school (such as Bikeability for children, their families and carers, Walk to School Outreach and School Streets), to improve wellbeing, reduce carbon emissions and improve air quality

support an EA pilot investigating offsetting carbon emissions from their flood scheme construction work by contributing to tree planting and greening on available, nearby school land

By 2030:

through the National Education Nature Park and cross-government collaboration, we will have significantly increased the biodiversity on the education estate and implemented 'Green Corridors' for access to education settings, improving climate resilience, physical and mental wellbeing, and protecting and enhancing wildlife and ecosystems along the routes between nurseries, schools, colleges and universities

we will have supported the Department for Transport to increase active travel to school, contributing to the Prime Minister's vision that half of all short journeys in towns and cities will be walked or cycled

we will have minimised barriers to nature in school building design programmes and education building standards, increasing opportunities to connect their users to nature

4. Heating solutions

The building energy efficiency survey indicates that approximately 60% of energy use in education settings is associated with high carbon intensity fuels such as natural gas, coal and oil. There is a need to reduce demand for heating and hot water use and to deliver through sustainable means.

We are launching 10 pilots to test our Energy Pods as a way of providing off-site manufactured, low-carbon, heating solutions on the existing school and college estate. The results of these pilots will inform our strategic approach to condition, safety and sustainability for the education estate and provide important research data for education and public sector net-zero projects.

In 2022 we will:

test the feasibility of replacing school boilers with ground or air source heat solutions, and use the learning to consider how this can be scaled up to accelerate decarbonisation between 2025 and 2035

continue to work with BEIS to help education settings access the Public Sector Decarbonisation Scheme to support them in replacing fossil fuel heating systems with low carbon heating

5. Water strategy

In partnership with EA, water companies and local authorities we will deliver our water strategy to improve:

We will work together to improve learning and awareness of risk and resilience and encourage all education settings to sign up to available weather and flood warnings. For schools at highest risk, we will share a flood risk plan template to set out emergency plans. In addition:

by 2023, the risk of school flood will be displayed alongside emissions and biodiversity on the National Education Nature Park

by 2026, we will have reduced the flood risk in over 800 schools through our Water Strategy initiatives

we will work with Ofwat and Market Operator Services Ltd to ensure their regulatory mechanisms and incentives enable and drive the water market to improve efficiency across the education estate and, should this prove successful, by 2030 we will have worked with water wholesalers and retailers to improve water efficiency at the 10,000 least water-efficient schools, significantly reducing usage and spend

we will align strategic action with the Environment Agency's National Flood and Coastal Erosion Risk Management Strategy to ensure education settings are ready for, and resilient to, flooding and coastal change

6. Reporting Frameworks, Reporting Processes and Targets

With increased legislation on net zero, environment and nature, it is inevitable that we are going to have to ensure consistent reporting on the activity of our sectors.

The government's Net Zero Strategy commits to legislate reporting of emissions if insufficient progress is made voluntarily. We will work with BEIS in the development of guidance on monitoring and reporting for the education sector and we will look to support and facilitate reporting.

We will work with energy providers to receive data from the school estate directly (unless individual schools choose to opt out), so that by 2024 all schools are reporting their emission via a standardised framework. The installation of smart meters will improve the accuracy of this data.

We have supported the Queen's Jubilee Challenge for the further and higher education sectors to accelerate a sector-led review. This review will enable all further and higher education settings to report their emissions via a standardised and comparable framework by 2024. From 2025 we will publish targets and institutional progress for the further and higher education sectors. We will also work with nurseries and schools to set standardised reporting frameworks and implement effective data-gathering mechanisms. With the campaign Let's Go Zero, we will set targets for schools between 2025 and 2035.

Action area 4: Operations and supply chains

We have a valuable opportunity to drive change by introducing children and young people to more sustainable practices, such as:

We can inspire and instil habits that they will take into their wider communities and adult lives. Sector representatives have also confirmed that they have had the greatest results in driving sustainability in their operational processes when action has been linked to learning.

Coordination and leadership are required to bring this type of change. We recognise how much is already being done across the sectors by enthusiastic individuals, governance boards and leadership teams. They are driving effective whole-setting approaches to the challenges of sustainability and climate change - not only in teaching and learning or the decarbonisation of the estate - but in the way settings are operated and regulated.

Within DfE we will also publish and monitor progress against a DfE Corporate Sustainability Strategy. This will demonstrate how we will embed sustainability across everything we do - from how we maintain and use our office estate to our decision-making and business processes. We will align action with this strategy, the Greening Government Commitments and our overall organisational strategy.

By 2023 we will:

start rolling-out carbon literacy training for at least one person in every locally maintained nursery, school, college and university to build their knowledge of climate change, access to public funds, engagement with the nature park and Climate Leaders Award, understand emissions reporting and how to develop a climate action plan so that they can share learning and training within their own setting as appropriate, for example with leaders, support staff, care takers, cooks and teachers

support the work of the National Climate Education Action Plan to bring together universities with STEM Learning to identify Climate Ambassadors to support schools and further education institutions in the delivery of their sustainability and climate change action plans

update Good Estate Management for Schools ( GEMS) guidance to include the latest sustainability and climate change advice, covering topics such as efficient waste and water management - we will signpost to other relevant third-party materials

encourage and support education settings to procure from companies that commit to achieving net zero by 2050 and have a plan in place, showing how they will meet this target

ensure sustainability is part of the assessment and validation criteria for including suppliers on procurement frameworks, to support sustainable purchasing of products and services, including energy, in schools

support schools to buy from procurement frameworks that offer sustainable goods and services

work with education settings and partners to share best practice for delivering and improving uptake of nutritionally balanced, affordable and sustainable meal choices

pilot a food curriculum and whole-school approach to food, promoting accountability and transparency of school food arrangements by encouraging schools to complete a statement on their school websites, which sets out their whole-school approach to food, with voluntary reporting initially, and the intention that this will become mandatory when schools can do this effectively

pilot new training for school governors on a whole-school approach to food as set out in the forthcoming Government Food Strategy (school governors have a responsibility to ensure compliance and should appropriately challenge the headteacher and the senior leadership team to ensure the school is meeting its obligations)

support schools to drive up their sustainable practices in line with the school food standards, particularly in relation to school food contracts and their food preparation and practice, with consideration of the ingredients they use and their environmental impact

encourage and support education settings to gather data and take action on food waste, and to share their evidence-based best practice for sustainable waste prevention and management

work with DEFRA and the Waste and Resources Action Programme ( WRAP) to share resources for schools and children that support food waste prevention

support education settings in aligning with DEFRA 's Resources and Waste Strategy to reduce all waste, moving away from single-use items and towards reusable alternatives where possible

continue to encourage all settings to drive initiatives that promote the circular economy - providing guidance to reduce reliance on single-use items in favour of alternatives, such as reusable nappies, as well as recycled materials, second-hand uniforms and other consumables, such as sustainable cutlery and cleaning products

continue to offer a range of free sustainable period products to learners in schools and colleges across England and use the success of this programme as a catalyst for similar initiatives

seek opportunities to rationalise deliveries to education settings, through minimum order values or changes to delivery models, and encourage settings to consider a rounded approach to providing food, including practical guidance on consolidating food deliveries and sourcing food locally where possible

By 2025:

all education settings will have nominated a sustainability lead and put in place a climate action plan

all services and products procured by DfE for schools through the 'get help buying for schools' programme will go through environmental procurement frameworks

we will review existing procurement frameworks and identify those where net-zero targets can be applied, highlighting these frameworks to education providers as sustainable and net zero-compliant buying frameworks

we will ensure there is a clear route to market and associated support for schools to buy goods and services that help them to generate their own energy or reduce their existing energy consumption, for example, solar panels and LED light bulbs

we will eradicate single-use plastics and encourage the use of reusable and recyclable materials in schools and encourage all other education settings to match this target

we will work with DEFRA to provide guidance to education settings for meeting the requirements of the Environment Act 2021 - this will require settings to arrange for the collection of a core set of materials for recycling, including glass, paper and card, plastic, metal and food waste, increasing the amount of material recycled and diverted from landfill

we will develop a sustainable assessment model in partnership with the Standards and Testing Agency, considering the environmental impact of digital testing and the trade-offs with existing testing methods

we will continue to support DEFRA with implementing the Resource and Waste Strategy, to eliminate unnecessary plastics in education settings

By 2030:

education settings will be supporting a circular economy through their participation in the Climate Leaders Award and National Education Nature Park - for example, by using food waste to produce compost that can then be used directly or in the local community

Action area 5: International

We set out our vision for the United Kingdom education sector to be world leading in sustainability and climate change by 2030. In achieving this vision, we want to both inspire and respond to international action and make a difference to children and young people all over the world. There is no time to lose: extreme weather events are increasing in severity and frequency, and Safe Schools: The Hidden Crisis shows that it is disrupting the education of nearly 40 million children a year worldwide. Children in the UK say they are worried about how climate change is affecting children and families in developing countries.

The UK Foreign Policy Integrated Review positions climate change as the UK's number one international priority. Education is one of our nation's greatest strengths. We will take positive international action to enhance climate change and sustainability learning in and outside of the classroom and, in particular, through DfE 's key bilateral relationships.

In doing so we will work across government with the:

Foreign, Commonwealth and Development Office (FCDO)

Department for Business and Trade (DBT)

Cabinet Office

Department for Energy Security and Net Zero

British Council

Together we will provide young people and adults everywhere with the knowledge and skills needed to:

Our action will align with and support the UK Government's International Education Strategy and supplement the great work the UK already does overseas, led by the FCDO, through education partnerships with around 20 poorer countries as leading donor to the Global Partnership for Education ( GPE) and Education Cannot Wait Fund, to support countries to respond to weather related disasters and make education systems more resilient.

The international action set out below represents the whole of the UK. Scotland, Wales, Northern Ireland and England each bring unique offers to an international strategy, and we will work closely with the governments of each nation to champion them effectively.

At COP26 we put education on a global stage, bringing education and environment ministers together to pledge action on climate education. We will continue to encourage other countries to make pledges. We will also track progress on our international commitments to complement and promote the Glasgow Works Programme on Action for Climate Empowerment ( ACE) negotiations agreed by countries at COP26 through our work and partnerships.

This work programme positions education as integral to the achievement of long-term climate goals, and calls for an approach which is grounded in principles of collaboration, flexibility and inclusivity.

Champion education and learning on the global stage

We will promote education as a key tool for tackling climate change through:

We will elevate the voices of youth in these discussions, as well as the rich experiences of other countries and stakeholders.

We will work across government and convene international partners to build a coalition for action for sustainable education. We will:

work closely with youth partners and multilateral institutions, such as UNESCO, UNEP and OECD, and in the G7 and G20 to exchange good practice through global discussions on climate education, learning and sustainable development

showcase our Sustainability and Climate Change Strategy and activities, sharing our strategy as a model for others to learn from

build an international evidence base on the support and solutions needed for a range of contexts

share our latest lessons, challenges, insights and evidence from the delivery of all action areas of our strategy

learn from others, including those working at local, national, regional and international levels

Trade and exports

We will provide expertise, services, trade and exports overseas to support others in achieving their net-zero targets, increasing resilience and adaptation, and improving climate education. We will work with FCDO, DBT and the UK international trade network to:

identify appropriate export opportunities for our climate learning programmes, including the National Education Nature Park and Climate Leaders Award, sharing our expertise on flood resilience and flood risk assessments, and export innovative sustainable products such as the Gen Zero Platform and Biophilic Primary School

continue work with the further and higher education sectors to ensure prestigious green-skills learning, training and research opportunities are developed to attract overseas students

International initiatives

We will identify opportunities to broaden our actions to share innovation and provide opportunities for young people from across the world to connect and learn, ensuring we align with the UK government's Child Safeguarding Due Diligence guidance. We will:

Leadership, engagement and next steps

Leadership

In recognition of the importance of this agenda to the Department for Education, the Secretary of State for Education will also be Climate Change Minister for the Department for Education. The Secretary of State for Education will also oversee the Climate Leaders Award.

The Secretary of State for Education will be supported in his role of Climate Change Minister for the department by:

Baroness Barran - net zero of DfE-delivered buildings, procurement and supply chains (this also includes the biodiversity of the education estate)

Minister Donelan and Minister Burghart - green skills, green jobs and net zero in further education and higher education

Minister Walker - climate education

Minister Quince - physical and mental wellbeing

DfE will nominate a non-executive director for climate change. They will champion climate issues on departmental boards and provide further leadership and expertise to support the department with its unique climate mission.

Engagement

From November 2021 to March 2022, sector representatives and young people reflecting a diverse range of voices, backgrounds and experiences brought together feedback on this strategy from those they represent.

We will continue to engage formally through a quarterly youth panel and user group as we develop and deliver the strategy, to ensure it meets the needs of those it serves.

Next Steps

Each year we will publish progress against the strategy and set out new action.
The following actions will support this:

Vision: the United Kingdom is the world-leading education sector in sustainability and climate change by 2030.

Strategic aims

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Sustainability and climate change: a strategy for the education and children's services systems

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This strategy applies to: the Department for Education (DfE), its agencies and public bodies the education and children's services systems in England - including: early years schools (and independent schools where applicable) further education higher education children's social care DfE has an important role to play in all aspects of sustainability.

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Supporting quality education for sustainable development (ESD) through excellence in leadership, teaching and learning for the further education (FE) and training sector #ESDinFE

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Sustainability case studies (AI generated)

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Sustainability case studies (AI generated)

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EPA assessment plan

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Halloween Instragram competition ... £20 Amazon voucher up for grabs!

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To help with EPA prep - PD Professionalism element - Distinction questions / statements