On 24 July 2023, the House of Lords is due to debate the following motion:

Lord Ravensdale (Crossbench) to move that this House takes note of the ongoing development of advanced artificial intelligence, associated risks and potential approaches to regulation within the UK and internationally.

1. What is artificial intelligence?

Artificial intelligence (AI) can take many forms. As such, there is no agreed single definition of what it encompasses. In broad terms, it can be regarded as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. According to IBM, the current real-world applications of AI include:

  • extracting information from pictures (computer vision)
  • transcribing or understanding spoken words (speech to text and natural language processing)
  • pulling insights and patterns out of written text (natural language understanding)
  • speaking what has been written (text to speech, natural language processing)
  • autonomously moving through spaces based on its senses (robotics)
  • generally looking for patterns in large amounts of data (machine learning)

In banking, for example, AI is currently used to detect and flag suspicious activity to a bank’s fraud department, such as unusual debit card usage and large account deposits. The NHS also reports that AI is being used to benefit people in health and care by analysing X-ray images to support radiologists in making assessments and helping clinicians read brain scans more quickly, by supporting people in ‘virtual wards’, who would otherwise be in hospital to receive the care and treatment they need, and through remote monitoring technology such as apps and medical devices which can assess patients’ health and care while they are being cared for at home.

To achieve this, AI systems rely upon large datasets from which they can decipher patterns and correlations, thereby enabling the system to ‘learn’ how to anticipate future events. It does this by relying upon and/or creating algorithms based on the dataset which it can use to interpret new data. This data can be structured, such as bank transactions, or unstructured, such as enabling a driverless car to respond to the environment around it.

The different forms that AI can take range from so-called ‘narrow’ AI designed to perform specific tasks to what is known as ‘strong’ or ‘general’ AI with the capacity to learn and reason. The House of Commons Library recently drew upon research from Stanford University and other sources to offer the following definitions:

  • Narrow AI is designed to perform a specific task (such as speech recognition), using information from specific datasets, and cannot adapt to perform another task. These are often tools that aim to assist, rather than replace, the work of humans.
  • Artificial general intelligence (AGI—also referred to as ‘strong’ AI) is an AI system that can undertake any intellectual task/problem that a human can. AGI is a system that can reason, analyse and achieve a level of understanding that is on a par with humans; something that has yet to be achieved by AI. The US computer scientist Nils John Nilsson, for example, proposed that one way to test if a system had achieved AGI was if it could successfully learn the skills to perform the different jobs “ordinarily performed by humans”, from “knowledge work” (such as a Library assistant) to “manual labour” (such as a roofer).
  • Machine learning is a method that can be used to achieve narrow AI; it allows a system to learn and improve from examples, without all its instructions being explicitly programmed. It does this by finding patterns in large amounts of data, which it can then use to make predictions (for example what film or TV programme you might like to watch next on a streaming platform). The AI can then independently amend its algorithm based on the accuracy of its predictions.
  • Deep learning is a type of machine learning whose design has been informed by the structure and function of the human brain and the way it transmits information. The application of deep learning can be seen in ‘foundation models’, of which ‘large language models (LLMs)’ such as ChatGPT, are one example. The term refers to those models that are trained on very large, unlabelled datasets and which can be adapted to do a wide range of tasks, despite not having been trained explicitly to do those tasks. In other words, the model can take information it has learnt about in one situation and apply it to another, different situation. Sometimes LLMs are refined or ‘fine-tuned’ (trained using additional data) to achieve a specific goal. ChatGPT, for example, has been fine-tuned to allow users to ask it a question, or make a request, and for it to generate “human-like text” in response.
Illustrative example: Text generated directly by AI

As a basis for comparison, when ChatGPT—known as a large language model or LLM, which is designed to use a wide knowledge base to provide responses to user prompts—is asked to define the key forms of AI it gives the following response:

  1. Narrow AI: Narrow AI, also known as weak AI, refers to AI systems designed to perform specific tasks or solve specific problems. These systems are limited in their scope and cannot exhibit general intelligence. Examples of narrow AI include voice assistants like Siri or Alexa, image recognition algorithms, and recommendation systems.
  2. General AI: General AI, also referred to as strong AI or human-level AI, is an AI system that possesses the ability to understand, learn, and perform any intellectual task that a human being can do. General AI would possess a broad range of cognitive abilities and be capable of reasoning, understanding natural language, learning new tasks, and adapting to different situations. While significant progress has been made in AI, true general AI remains a theoretical concept and has not yet been achieved.
  3. Machine learning: Machine learning is a subset of AI that focuses on the development of algorithms and models that enable computer systems to learn and make predictions or decisions without being explicitly programmed. Machine learning algorithms are trained on large datasets and can recognize patterns, make predictions, and continuously improve their performance over time. Popular machine learning techniques include supervised learning, unsupervised learning, and reinforcement learning.
  4. Deep learning: Deep learning is a subfield of machine learning that employs artificial neural networks, inspired by the structure and function of the human brain, known as deep neural networks. Deep learning models are capable of learning hierarchical representations of data and can process large amounts of complex information, such as images, audio, and text. Deep learning has been highly successful in tasks like image and speech recognition, natural language processing, and autonomous driving.
  5. Natural language processing (NLP): Natural language processing is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP algorithms analyse and process text and speech data, enabling tasks such as language translation, sentiment analysis, chatbots, and voice assistants.
  6. Robotics and automation: Robotics combines AI and physical systems to create machines or robots capable of performing physical tasks and interacting with the physical world. AI-powered robots can be designed for various applications, such as industrial automation, healthcare assistance, exploration, and even domestic tasks.

As this example demonstrates these systems are capable of generating sophisticated responses. However, LLMs such as ChatGPT and Google Bard have sometimes been found to have an issue with “hallucinating” inaccurate information and providing it in response to a user prompt.

As noted by the UK government in its ‘National AI strategy’, AI is the fastest growing deep technology in the world, with “huge potential to rewrite the rules of entire industries, drive substantial economic growth and transform all areas of life”. Such transformative technology brings both risks and benefits, as explored below.

2. Ongoing development of AI: Potential benefits and risks

2.1 Current contribution of AI to the UK economy

The AI sector is already providing a significant contribution to the UK economy. The ‘Artificial intelligence sector study 2022’ published by the Office for Artificial Intelligence and the Department for Science, Innovation and Technology found that there were 3,170 AI companies working in the UK which generated £10.6bn in AI-related revenue. The sector also employed more than 50,000 people in AI-related roles, generating £3.7bn in gross value added (GVA). Further, it had secured £18.8bn in private investment since 2016.

Taken from the same publication, figure 1 illustrates the size profile of AI companies in the UK:

Figure 1. Size profile of UK AI companies, 2022

Figure 1 shows the relative size of businesses operating with the UK AI sector. It shows for example that 60% of firms in the sector were micro in size.
(Source: Department for Science, Innovation and Technology and Office for Artificial Intelligence, ‘Artificial Intelligence sector study 2022‘, March 2023)

The categories in the graph are defined as follows: large business have more than 250 employees; medium, 50–249; small, 10–49; and micro, less than 9.

Other key findings from the report included (emphasis in original):

  • Of the 3,170 active companies identified through the study, 60% are dedicated AI businesses and 40% are diversified ie, have AI activity as part of a broader diversified product or service offer.
  • Compared to similar studies into other emerging technology sectors, a greater proportion of diversified AI companies have been identified, highlighting the broad scope for development of AI technology applications by established technology companies across sectors.
  • On average 269 new AI companies have been registered each year since 2011, with a peak in new company registrations in the same year as the AI sector deal in 2018 (429 companies).
  • Together, the data on company size and business model suggest that dedicated AI companies are both smaller and more dependent on AI products for revenue. Diversified AI companies are typically larger and likely to generate a greater proportion of revenues from less capital-intensive provision of AI-related services.
  • London, the South East and the East of England account for 75% of registered AI office addresses, and also for 74% of trading addresses. Just under one-third of AI companies with a registered address outside of London, the South East and the East of England still have a trading presence in those regions, highlighting the apparent significance of those regions to development of the UK AI sector to date. [These findings are illustrated in figure 4 below.]
  • While absolute numbers are smaller, the study has identified more notable proportions of wider regional AI activity in automotive, industrial automation and machinery; energy, utilities and renewables; health, wellbeing and medical practice; and agricultural technology.
  • In the most recent financial year (2021/22), annual revenues generated specifically from AI-related activity by UK AI companies totalled an estimated £10.6bn, split approximately 50/50 between dedicated and diversified companies.
  • Across both dedicated and diversified AI companies, study estimates suggest that there are 50,040 full time equivalents (FTEs) employed in AI-related roles, 53% of which are within dedicated AI companies.
  • Based on a combination of official company data, survey responses and associated modelling, AI companies are estimated to contribute £3.7bn in GVA to the UK economy. For large companies the GVA-to-turnover ratio is 0.6:1 (ie, for every £1 of revenue, large AI companies generate 60p in direct GVA). GVA-to-turnover ratios among small and medium sized enterprises (SMEs) are much lower (0.2:1 for medium-sized companies and negative for small and micro businesses), which reflects the capital-intensive, high R&D nature of deep technology development.
  • Since 2016, AI companies have secured a total of £18.8bn in private investment. 2021 was a record year for AI investment, with over £5bn raised across 768 deals, representing an average deal size of £6.7mn. Further, AI investment increased almost five-fold between 2019 and 2021.
  • In 2022 dedicated AI companies secured a higher average deal value than diversified companies for the first time. However, data on AI investment by stage of evolution may also be signalling some tightening of investment available to seed and venture stage companies and, given the significance of private investment for AI technology development evidenced by data on revenues and GVA, this could pose a risk to realising the potential within early-stage AI companies.
  • The study highlighted a notable opportunity for companies operating in the AI implementation space to build teams of AI implementation experts that can support AI adoption opportunities across sectors. This adoption opportunity is supported by investment data, which highlights that in 2022 investments were made in 52 unique industry sectors, compared to investments across just 35 different sectors in 2016.

A selection of this data is illustrated in the charts below, again taken from the ‘Artificial intelligence sector study 2022’:

Figure 2. Breakdown of machine learning companies by industry and sub-industry in the UK, 2022

Figure 2 shows the breakdown of Machine Learning Companies by Industry and Sub-Industry in the UK

Figure 3. UK AI revenue by firm size, 2022

Figure 3 illustrates UK AI revenue by firm size

Figure 4. Regional AI activity in the UK (by registered addresses), 2022

Figure 4 shows regional AI activity in the UK by registered addresses

2.2 Potential benefits and risks of AI

The variety of potential applications of AI also means that there is a similarly wide number of potential benefits and risks to using such technology. The European Parliament summarises the potential benefits of AI at a societal level as follows:

  • AI could help people with improved health care, safer cars and other transport systems, tailored, cheaper and longer-lasting products and services. It can also facilitate access to information, education and training. […] AI can also make workplace safer as robots can be used for dangerous parts of jobs, and open new job positions as AI-driven industries grow and change.
  • For businesses, AI can enable the development of a new generation of products and services, and it can boost sales, improve machine maintenance, increase production output and quality, improve customer service, as well as save energy.
  • AI used in public services can reduce costs and offer new possibilities in public transport, education, energy and waste management and could also improve the sustainability of products.
  • Democracy could be made stronger by using data-based scrutiny, preventing disinformation and cyber attacks and ensuring access to quality information […].
  • AI is predicted to be used more in crime prevention and the criminal justice system, as massive datasets could be processed faster, prisoner flight risks assessed more accurately, crime or even terrorist attacks predicted and prevented. In military matters, AI could be used for defence and attack strategies in hacking and phishing or to target key systems in cyberwarfare.

However, the article also noted some of the risks of AI. These included liability, including who is responsible for any harms or damage caused by the use of AI. Similarly, in an article on Forbes’ website, the futurist Bernard Marr also suggested that the biggest risks of AI at a broad level are:

  • A lack of transparency, particularly regarding the development of deep learning models (including the so-called ‘Black Box’ issue where AI generates unexpected outputs and human scientists and developers are not clear why it has done so).
  • Bias and discrimination, particularly where AI systems inadvertently perpetuate or amplify societal bias.
  • Privacy concerns, particularly given the capacity of AI to analyse large amounts of personal data.
  • Ethical concerns, especially concerning the challenges inherent to instilling moral and ethical values in AI systems.
  • Security risks, including the development of AI-driven autonomous weaponry.
  • Concentration of power, given the risk of AI development being dominated by a small number of corporations.
  • Dependence on AI, including the risk that an overreliance on AI leads to a loss of creativity, critical thinking skills and human intuition.
  • Job displacement, the potential for AI to abolish the need for some jobs (whilst potentially generating the need for others).
  • Economic inequality, and the risk that AI will disproportionally benefit wealthy individuals and corporations.
  • Legal and regulatory challenges, and the need for regulation to keep pace with the rapid development of innovation.
  • An AI arms race, where companies, and countries, compete to be first to generate new capabilities at the expense of ethical and regulatory concerns.
  • Loss of human connection, and the fear that a reliance on AI-driven communication and interactions could lead to diminished empathy, social skills and human connections.
  • Misinformation and manipulation, and the risk that AI-generated content drives the spread of false information and the manipulation of public opinion.
  • Unintended consequences, particularly around the complexity of AI systems and a lack of human oversight leading to undesirable outcomes.
  • Existential risks, including the rise of artificial general intelligence (AGI) that surpasses human intelligence and raises long term risks for the future of humanity.

On the risk of misinformation and manipulation, several commentators have already suggested that elections in 2024, particularly the US presidential election, may be the first elections where the campaigning process is significantly affected by AI.

2.3 Potential impact on the UK employment market

A report commissioned by the government from consultancy firm PWC in 2021 found that 7 percent of jobs in the UK labour marker were at high risk of being automated in the next five years. This rose to 30 percent after 20 years:

Our base case estimate is that around 7 percent of existing UK jobs could face a high (over 70 percent) probability of automation over the next five years, rising to around 18 percent after 10 years and just under 30 percent after 20 years. This is within the range of estimates from previous studies and draws on views from an expert workshop on the automatability of occupations and detailed analysis of OECD [Organisation for Economic Co-operation and Development] and ONS [Office for National Statistics] data on how this is related to the task composition and skills required for different occupations.

The manufacturing sector was highlighted in the report as being at risk of losing the most jobs over the next 20 years, with job losses also expected in transport and logistics, public administration and defence, and the wholesale and retail sectors. In contrast, the health and social work sector was highlighted as most likely to see the largest job gains, while gains were also expected in the professional and scientific, education, and information and communications sectors. Jobs in lower paid clerical and process-orientated roles were most likely to be at risk of being lost. In contrast, the report suggested there would be job gains in managerial and professional occupations.

The report concluded that the most plausible assumption is that the long-term impact of AI on employment levels in the UK would be broadly neutral, but that the potential impacts within that umbrella were unclear.

More recent analyses of AI, particularly since the release of LLMs such as ChatGPT and Google Bard, have questioned whether the impact of AI will indeed be felt most in lower-paid/manual occupations. Analysis published in March 2023 by OpenAI itself, the creator of ChatGPT, suggested that higher wage occupations tend to be more exposed to LLMs. Its analysis also found that within this there will be differentiation depending on the nature of the tasks involved:

[T]he importance of science and critical thinking skills are strongly negatively associated with exposure, suggesting that occupations requiring these skills are less likely to be impacted by current LLMs. Conversely, programming and writing skills show a strong positive association with exposure, implying that occupations involving these skills are more susceptible to being influenced by LLMs.

On 21 April 2023, the House of Commons Business, Energy and Industrial Strategy Committee published a report on post-pandemic economic growth and the UK labour markets. This report highlighted the impact that AI could have on productivity within the UK. It refers to research from Deloitte that found that “by 2035 AI could boost UK labour market productivity by 25%”, and that “four out of five UK organisations said that use of AI tools had made their employees more productive, improved their decision-making, and made their process more efficient”.

It also argued that AI and related technologies may have a positive impact on helping people access the labour market who have otherwise found it difficult to find and stay in employment, such as disabled people.

Estimates of the impact of AI on the UK and the world economy continue to be published regularly as these products develop. Recent examples include research from McKinsey which suggested that generative AI could add value equivalent to the UK’s entire GDP to the world economy over the coming years:

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6tn to $4.4tn annually across the 63 use cases we analysed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1tn. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.

2.4 Case study: Potential impact on the knowledge and creative industries (House of Lords Communications and Digital Committee report, January 2023)

There are potential applications of AI across almost every sphere of human life and as a result it would be impossible to examine them all here. However, in January 2023, the House of Lords Communications and Digital Committee examined the potential impact of AI on the creative industries in the UK as part of a wider examination of the sector, which provides an illustrative example.

The committee heard evidence that new technologies and the rise of digitised culture will change the way creative content is developed, distributed and monetised over the next five to 10 years. The committee drew particular attention to intellectual property (IP), the protection of which it said was vital to much of the creative industries, and the impact upon it of the rise of AI technologies, particularly text and data mining of existing materials used by generative AI models to learn and develop content.

The committee also drew attention to recently proposed reforms to IP law:

The government’s proposed changes to IP law provided an illustrative example of the tension between developing new technologies and supporting rights holders in the creative industries. In 2021 the Intellectual Property Office (IPO) consulted on the relationship between IP and AI. In 2022 the IPO set out its conclusions, which included “a new copyright and database right exception which allows text and data mining for any purpose”.

The committee suggested that such proposals were “misguided” and took “insufficient account of the potential harm to the creative industries”. Arguing that the development of AI was important, but “should not be pursued at all costs”, the committee argued that the IPO should pause its proposed changes to the text and data mining regime “immediately”. The committee added that the IPO should conduct and publish an impact assessment on the implications for the creative industries, and if this assessment found negative effects on businesses in the creative industries, it should then pursue alternative approaches, such as those employed by the European Union. (The European Union’s approach is examined in section 5.1 of this briefing.)

The committee also warned against the use of AI to generate, reproduce and distribute creative works and image likenesses which went against the rights of performers and the original creators of the work.

In its response to the committee, the government said that, “in light of additional evidence” of the impact on the creative sector, the government “will not be proceeding” with the proposals for an exception for text and data mining of copyrighted works. Instead, the government said it would work with users and rights holders to produce a “code of practice by the summer [2023]” on text and data mining by AI.

There are several court challenges underway on the use of existing written content and images to train generative AI. For example, authors Paul Tremblay and Mona Awad have launched legal action in the United States against OpenAI alleging that copies of their work were used unauthorised to develop its ChatGPT LLM. The debate on how best to protect copyright and creative careers such as writing and illustrating continues. The Creators’ Rights Alliance (CRA), which is formed of bodies from across the UK cultural sector, contends that current AI technology is accelerating and being implemented without enough consideration of issues around ethics, accountability, and economics for creative human endeavour.

The CRA argues that there should be a clear definition and labelling of what constitutes solely AI generated work and work made with the intervention of creators, and that the distinct characteristics of individual performers and artists should be protected. At the same time, it said that copyright should be protected, including no data mining of existing work without consent, and that there should be transparency over the data used to create generative AI. The CRA also called for increased protection for creative roles such as visual artists, translators and journalists, or these roles could be lost to AI systems.

3. Calls for rapid regulatory adaptation

The potential benefits and harms of AI have led to calls for governments to adapt quickly to the changes AI is already delivering and the potentially transformative changes to come. These include calls to pause AI development and for countries including the UK to deliver a step-change in regulation, potentially before the technology passes a point when such regulation can be effective. The chief executive of Google, Sundar Pichai, is one example of a leading technology figure who has warned about the potential harms of AI and called for a suitable regulatory framework.

This is a fast-moving area and this briefing concentrates on reports published since the beginning of 2023. For an exploration of publications and milestones before this time, including the work of the House of Lords Committee on Artificial Intelligence, see the earlier House of Lords Library briefing ‘Artificial intelligence policy in the UK: Liaison Committee report’, published in May 2022.

3.1 Letter from key figures in AI, science and technology calling for a pause in AI development (March 2023)

In March 2023, more than 1,000 artificial intelligence experts, researchers and backers signed an open letter calling for an immediate pause on the creation of “giant” AIs for at least six months, so the capabilities and dangers of such systems can be properly studied and mitigated.

The signatories included engineers from Amazon, DeepMind, Google, Meta and Microsoft, as well as academics and prominent industry figures such as Elon Musk, who co-founded OpenAI (the research lab responsible for ChatGPT and GPT-4), Emad Mostaque, who founded London-based Stability AI, and Steve Wozniak, the co-founder of Apple.

The letter noted that, given the potential of advanced AI, it “should be planned for and managed with commensurate care and resources”. It said that “unfortunately, this level of planning and management is not happening”, even though recent months have seen “AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one—not even their creators—can understand, predict, or reliably control”.

The letter added (emphasis in the original):

Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Should we let machines flood our information channels with propaganda and untruth? Should we automate away all the jobs, including the fulfilling ones? Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us? Should we risk loss of control of our civilization? Such decisions must not be delegated to unelected tech leaders. Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system’s potential effects. OpenAI’s recent statement regarding artificial general intelligence, states that “at some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models”. We agree. That point is now.

The Future of Life Institute, which coordinated the production of the letter, also published a policy paper entitled ‘Policy making in the pause’, which offered the following recommendations to govern the future of AI development:

  1. mandate robust third-party auditing and certification for specific AI systems
  2. regulate access to computational power
  3. establish capable AI agencies at the national level
  4. establish liability for AI-caused harms
  5. introduce measures to prevent and track AI model leaks
  6. expand technical AI safety research funding
  7. develop standards for identifying and managing AI-generated content and recommendations

However, not everyone shares the perspective that a pause is either necessary or practical. For example, BCS, the chartered institute for IT, has said this would only result in an “asymmetrical pause” as bad actors would ignore it and seize the advantage. Instead, the institute said that humanity would benefit from ethical guardrails around AI rather than halt any development. The chief executive of the BCS, Rashik Parmar, said: “we can’t be certain every country and company with the power to develop AI would obey a pause, when the rewards for breaking an embargo are so rich”.

Instead, BCS has issued a report outlining how AI can be helped to “grow up responsibly”, such as by making it part of public education campaigns and ensuring it is clearly labelled whenever it is used. Among the paper’s key recommendations are:

  • Organisations should be transparent about their development and deployment of AI, comply fully with applicable laws (eg in relation to data protection, privacy and intellectual property) and allow independent third parties to audit their processes and systems.
  • There should be clear and unambiguous health warnings, labelling and opportunities for individuals to give informed consent prior to being subject to AI products and services.
  • AI should be developed by communities of competent, ethical, and inclusive information technology professionals, supported by professional registration.
  • AI should be supported by a programme of increased emphasis on computing education and adult digital skills and awareness programmes to help the general public understand and develop trust in the responsible use of AI, driven by government and industry.
  • AI should be tested robustly within established regulatory ‘sandboxes’ (as proposed in the government white paper examined in section 4 below).
  • The use of sandboxes should be encouraged beyond a purely regulatory need. For example to test the correct skills and registration requirements for AI assurance professionals and how best to engage with civic societies and other stakeholders on the challenges and opportunities presented by AI.

3.2 Joint report by Sir Tony Blair and Lord Hague of Richmond (June 2023)

On 13 June 2023, Sir Tony Blair, the former Labour Prime Minister, and William Hague (Lord Hague of Richmond), the former leader of the Conservative Party, released a joint report, ‘A new national purpose: AI promises a world-leading future of Britain’, which described AI as “the most important technology of our generation”.

The authors said that getting policy right on this issue was therefore “fundamental” and contended that it could “define Britain’s future”. The report noted that the potential opportunities were “vast”, including the potential to “change the shape of the state, the nature of science and augment the abilities of citizens”. However, like others, the two former party leaders also noted that the risks were “profound”.

As a result, the report called for urgent action, including a “radical new policy agenda and a reshaping of the state, with science and technology at its core”. Noting that AI is already having an impact and that the pace of change is only likely to accelerate in the coming years, the authors contend that “our institutions are not configured to deal with science and technology, particularly their exponential growth”. They said that it was “absolutely vital that this changes”. This included a reorientation in the way government is organised, works with the private sector, promotes research, draws on expertise and receives advice.

To achieve this, the report offered specific recommendations including:

  • Securing multi-decade investment in science-and-technology infrastructure as well as talent and research programmes by reprioritising large amounts of capital expenditure to this task.
  • Boosting how Number 10 operates, dissolving the AI Council and empowering the Foundation Model Taskforce by having it report directly to the prime minister.
  • Sharpening the Office for Artificial Intelligence so that it provides better foresight function and agility for government to deal with technological change.

The report also contended that the UK could become a leader in the development of safe, reliable and cutting-edge AI, in collaboration with its allies. The authors contended that the UK has an “opportunity to construct effective regulation that goes well beyond existing proposals yet is also more attractive to talent and firms than the approach being adopted by the European Union”.

Again, the report offered recommendations on how this could be achieved, including:

Creating Sentinel, a national laboratory effort focused on researching and testing safe AI, with the aim of becoming the “brain” for both a UK and an international AI regulator. Sentinel would recognise that effective regulation and control is and will likely remain an ongoing research problem, requiring an unusually close combination of research and regulation.

Finally, the report contended that the UK could pioneer the deployment and use of AI technology in the real world, “building next-generation companies and creating a 21st century strategic state”. To achieve this, the report recommended:

  • Launching major AI talent programmes, including international recruitment and the creation of polymath fellowships to allow top non-AI researchers to learn AI as well as leading AI researchers to learn non-AI fields and cross-fertilise ideas.
  • Requiring a tiered-access approach to compute provision under which access to larger amounts of compute comes with additional requirements to demonstrate responsible use.
  • Requiring generative-AI companies to label the synthetic media they produce as deepfakes and social-media platforms to remove unlabelled deepfakes.
  • Building AI-era infrastructure, including compute capacity and remodelling data, as a public asset with the creation of highly valuable, public-good datasets.

The report added that it was “critical to engage the public throughout all of these developments” to ensure AI development is accountable and give people the skills and chance to adapt.

4. Proposed regulatory approaches: UK

4.1 UK government approach to artificial intelligence

On 22 September 2021, the government published its ‘National AI strategy’, setting out its ten-year plan on AI. The strategy set out three high-level aims:

  • invest and plan for the long-term needs of the AI ecosystem to continue our leadership as a science and AI superpower
  • support the transition to an AI-enabled economy, capturing the benefits of innovation in the UK, and ensuring AI benefits all sectors and regions
  • ensure the UK gets the national and international governance of AI technologies right to encourage innovation, investment, and protect the public and our fundamental values

The Office for Artificial Intelligence, a unit within the Department for Science, Innovation and Technology (DSIT), is responsible for overseeing the implementation of the national AI strategy. There is also an AI Council, a non-statutory expert committee of independent members set up to provide advice to the government.

In July 2022, the government published a consultation paper on establishing a “pro-innovation” approach to AI. This was followed in March 2023 by a white paper and further consultation exercise, which contain several principles and proposals for regulatory reform which are discussed in detail in section 4.3 of this briefing.

4.2 Current regulatory environment for AI in the UK

The government argues that the UK is in a strong position to benefit from the development of AI “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”.

Ministers contend that UK laws, regulators and courts already address some of the emerging risks posed by AI technologies. However, they also concede that, while AI is currently regulated through existing legal frameworks like financial services regulation, some AI risks have arisen and will arise across, or in the gaps between, existing regulatory remits.

The government provides the following evaluation of where such risks might exist and how they could potentially be mitigated:

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. AI systems are also required by data protection law to process personal data fairly. 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, medical devices, and toys) 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 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.

In response to the 2022 consultation exercise cited above, the government reported that those working in the AI sector said 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”.

Further, the government said that respondents to the consultation had highlighted that, if regulators were 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”. Noting that small businesses and start-ups often do not have the resources to do both and the prevalence of such firms in the sector, the government argued that it was “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”.

4.3 Proposals for future regulatory reform: Government white paper

The government’s proposals for future regulatory reform were set out in the March 2023 white paper, ‘A pro-innovation approach to AI regulation’.

Noting that across the world countries and regions were beginning to draft the rules for AI, the white paper said that the UK “needs to act quickly to continue to lead the international conversation on AI governance and demonstrate the value of our pragmatic, proportionate regulatory approach”.

The white paper said that the government recognised both the rewards and risks of AI:

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 and undermine human rights.

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. 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.

The white paper said that responding to risk and building public trust were important drivers for regulation, but that clear and consistent regulation could also support business investment and build confidence in innovation.

Consequently, it said that the government would put in place a new framework to bring “clarity and coherence” to the AI regulatory landscape, which will harness AI’s ability to drive growth and prosperity and increase public trust in its use and application. In taking a “deliberately agile and iterative approach”, the government said that its framework was “designed to build the evidence base so that we can learn from experience and continuously adapt to develop the best possible regulatory regime”.

That framework is underpinned by five principles to “guide and inform the responsible development and use of AI in all sectors of the economy”. These are:

  • safety, security and robustness
  • appropriate transparency and explainability
  • fairness
  • accountability and governance
  • contestability and redress

On whether new legislation would be introduced to support these aims, the white paper said:

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.

However, the paper also added that “following this initial period of implementation”, the government anticipated introducing a statutory duty on regulators requiring them to have due regard to the principles. The paper added:

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.

Regarding the potential gaps between the remits of various regulators identified above, the white paper noted that the 2022 AI consultation paper proposed a small coordination layer within the regulatory architecture. However, the white paper noted that, while industry and civil society were reportedly supportive of the intention to ensure coherence across the AI regulatory framework, “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”.

Consequently, the white paper said that the government had identified several 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. These were:

  • 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.

The white paper said that these functions would not entail the creation of a new AI regulator:

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.

The white paper included a consultation exercise on the proposals, which ran for 12 weeks until 21 June 2023. The government is yet to publish an analysis of the responses received.

The government has already moved to dissolve the AI Council, as reported in the Times on 19 June 2023. It will be replaced by a new foundation model taskforce led by technology entrepreneur Ian Hogarth, which will spearhead the adoption and regulation of the technology in the UK. A statement released on 7 July 2023 by the Department of Science, Innovation and Technology said, with the terms of the current council members finishing, it would establish a wider group of expert advisers:

Since it was established in 2019, the AI Council has advised government on AI policy with regards to national security, defence, data ethics, skills, and regulation, which has played a key role in developing landmark policies including the National AI Strategy, and the recent AI regulation white paper. The council also supported the government’s early Covid-19 efforts, highlighting the immediate needs of the AI startup ecosystem and facilitating rapid intelligence-gathering that shaped government support for the tech sector in its pandemic response.

With the terms of the AI Council members coming to an end, the Department for Science, Innovation and Technology is establishing a wider group of expert advisers to input on a range of priority issues across the department, including artificial intelligence. This will complement the recently established foundation model taskforce, which will drive forward critical work on AI safety and research.

The taskforce has been given £100mn to develop a British foundational generative AI model akin to ChatGPT to be used in the health service and elsewhere.

Prime Minister Rishi Sunak  has also announced that the UK will host a global summit on safety in artificial intelligence in the autumn. Writing in the Guardian in June 2023, Dan Milmo and Kiran Stacey argued that this marked a distinct “change of tone” from the government, with ministers going from talking predominately about the benefits of AI to the risks of such innovation. In addition to the different regulatory regimes discussed in section 5 of this briefing, the Guardian article also reports that the G7 have agreed to create an intergovernmental forum called the ‘Hiroshima AI process’ to debate issues around these fast-growing tools.

4.4 Individual sectoral guidance on the use of artificial intelligence

Organisations within the public and private sectors are evaluating how to respond to AI and some have produced guidance on that approach. For example, the Cabinet Office published guidance on the use of generative AI by civil servants, particularly LLMs, on 29 June 2023.

Similarly, in March 2023, the Department for Education issued guidance on the use of generative AI in pre-university education. Noting that the technology provided both risks and opportunities for the sector, the key principles outlined in that document stated that educational institutions must continue to guard against misuse whilst seeking to take advantage of these benefits.

This was followed in June 2023 by the Russell Group of universities publishing a guidance note on the use of generative AI in higher education. Again, the perspective of the Russell Group was not that generative AI tools should be banned, but that universities would support staff and students to become AI literate whilst using these technologies ethically:

Our universities wish to ensure that generative AI tools can be used for the benefit of students and staff—enhancing teaching practices and student learning experiences, ensuring students develop skills for the future within an ethical framework, and enabling educators to benefit from efficiencies to develop innovative methods of teaching.

5. Other regulatory approaches: Examples from around the world

5.1 European Union

In contrast to the UK, the European Commission is proposing a ‘horizontal’ and ‘risks-based’ means of regulating, meaning that it plans to provide rules for AI across all sectors and applications focused on the anticipated risk of such innovations.

In April 2021, the European Commission proposed the AI Act, draft legislation setting out rules for governing AI within the EU. The AI Act would establish four levels of risk for AI: unacceptable risk, high risk, limited risk, and minimal risk. Different rules apply depending on the level of risk a system poses to fundamental rights.

The European Commission suggests that those risk categories would work in the following ways:

Unacceptable risk

All AI systems considered a clear threat to the safety, livelihoods and rights of people will be banned, from social scoring by governments to toys using voice assistance that encourages dangerous behaviour.

High risk

AI systems identified as high-risk include AI technology used in:

  • critical infrastructures (eg transport), that could put the life and health of citizens at risk
  • educational or vocational training, that may determine the access to education and professional course of someone’s life (eg scoring of exams)
  • safety components of products (eg AI application in robot-assisted surgery)
  • employment, management of workers and access to self-employment (eg CV-sorting software for recruitment procedures)
  • essential private and public services (eg credit scoring denying citizens opportunity to obtain a loan)
  • law enforcement that may interfere with people’s fundamental rights (eg evaluation of the reliability of evidence)
  • migration, asylum and border control management (eg verification of authenticity of travel documents)
  • administration of justice and democratic processes (eg applying the law to a concrete set of facts)

High-risk AI systems will be subject to strict obligations before they can be put on the market:

  • adequate risk assessment and mitigation systems
  • high quality of the datasets feeding the system to minimise risks and discriminatory outcomes
  • logging of activity to ensure traceability of results
  • detailed documentation providing all information necessary on the system and its purpose for authorities to assess its compliance
  • clear and adequate information to the user
  • appropriate human oversight measures to minimise risk
  • high level of robustness, security and accuracy

All remote biometric identification systems are considered high risk and subject to strict requirements. The use of remote biometric identification in publicly accessible spaces for law enforcement purposes is, in principle, prohibited.

Narrow exceptions are strictly defined and regulated, such as when necessary to search for a missing child, to prevent a specific and imminent terrorist threat or to detect, locate, identify or prosecute a perpetrator or suspect of a serious criminal offence.

Such use is subject to authorisation by a judicial or other independent body and to appropriate limits in time, geographic reach and the data bases searched.

Limited risk

Limited risk refers to AI systems with specific transparency obligations. When using AI systems such as chatbots, users should be aware that they are interacting with a machine so they can take an informed decision to continue or step back.

Minimal or no risk

The proposal allows the free use of minimal-risk AI. This includes applications such as AI-enabled video games or spam filters. The vast majority of AI systems currently used in the EU fall into this category.

These proposals are intended to provide a “future proof” approach, allowing rules to adapt to technological change. The European Commission also said that all “AI applications should remain trustworthy even after they have been placed on the market”, requiring ongoing quality and risk management by providers.

However, the draft legislation has been amended by the EU Council and European Parliament, reportedly after concerns that technology such as ChatGPT, which has a large number of potential uses, could have a correspondingly large variety of risk thresholds. Politico notes efforts to reform the draft AI Act by its original proposers, MEPs Brando Benifei and Dragoș Tudorache, and reports on the resistance in some areas to those changes:

In February [2023] the lead lawmakers on the AI Act, Benifei and Tudorache, proposed that AI systems generating complex texts without human oversight should be part of the “high-risk” list—an effort to stop ChatGPT from churning out disinformation at scale.

The idea was met with scepticism by right-leaning political groups in the European Parliament, and even parts of Tudorache’s own Liberal group. Axel Voss, a prominent centre-right lawmaker who has a formal say over Parliament’s position, said that the amendment “would make numerous activities high-risk, that are not risky at all”.

In contrast, activists and observers feel that the proposal was just scratching the surface of the general-purpose AI conundrum. “It’s not great to just put text-making systems on the high-risk list: you have other general-purpose AI systems that present risks and also ought to be regulated”, said Mark Brakel, a director of policy at the Future of Life Institute, a non-profit focused on AI policy.

In May 2023, the European Parliament reported that MEPs had amended the list of those systems which pose an unacceptable level of risk to people’s safety to include bans on intrusive and discriminatory uses of AI systems such as “real-time” remote biometric identification systems in publicly accessible spaces. They also expanded the classification of high-risk areas to include harm to people’s health, safety, fundamental rights or the environment. They also added AI systems to influence voters in political campaigns and in recommender systems used by social media platforms to the high-risk list.

In addition, changes included obligations for providers of foundation models, who would have to guarantee protection of fundamental rights, health and safety and the environment, democracy and rule of law. They would need to assess and mitigate risks, comply with design, information and environmental requirements and register in the EU database. Generative foundation models, like ChatGPT, would have to comply with additional transparency requirements, like disclosing that the content was generated by AI, designing the model to prevent it from generating illegal content and publishing summaries of copyrighted data used for training.

The final text of the AI act is set to be agreed by late 2023 or early 2024. In addition to the AI Act, the EU has already passed several pieces of legislation such as the Digital Services Act (DSA) and Digital Markets Act (DMA), and alongside the AI Act is developing a civil liability framework on adapting liability rules to the digital age and AI, and revising sectoral safety legislation (such as regulations governing the use of machinery, artificial intelligence and autonomous robots).

In its own assessment of the differences between the UK and EU regime, the UK government’s white paper said:

The EU has grounded its approach in the product safety regulation of the single market, and as such has set out a relatively fixed definition in its legislative proposals. Whilst such an approach can support efforts to harmonise rules across multiple countries, we do not believe this approach is right for the UK. We do not think that it captures the full application of AI and its regulatory implications. Our concern is that this lack of granularity could hinder innovation.

5.2 United States of America

Writing in April 2023, Alex Engler at the Brookings Institute contends that the US federal government’s approach to AI risk management can broadly be characterised as risk-based, sectorally specific, and highly distributed across federal agencies. Mr Engler suggests that, while there are advantages to this approach, it also contributes to the uneven development of AI policies. He argues that, while there are several guiding federal documents from the White House on AI harms, “they have not created an even or consistent federal approach to AI risks”.

At the same time, Mr Engler notes that the US has invested in non-regulatory infrastructure, such as a new AI risk management framework, evaluations of facial recognition software, and extensive funding of AI research.

Comparing the approach taken by the US and the European Union, Mr Engler notes that the EU approach to AI risk management, as outlined in section 5.1, is characterised by a more comprehensive range of legislation tailored to specific digital environments. He adds that this has led to more differences than similarities between the two approaches:

The EU and US strategies share a conceptual alignment on a risk-based approach, agree on key principles of trustworthy AI, and endorse an important role for international standards. However, the specifics of these AI risk management regimes have more differences than similarities. Regarding many specific AI applications, especially those related to socioeconomic processes and online platforms, the EU and US are on a path to significant misalignment.

The EU-US Trade and Technology Council has demonstrated early success working on AI, especially on a project to develop a common understanding of metrics and methodologies for trustworthy AI. Through these negotiations, the EU and US have also agreed to work collaboratively on international AI standards, while also jointly studying emerging risks of AI and applications of new AI technologies.

For Mr Engler, more collaboration between international partners will be crucial, as governments implement the policies that will be foundational to the democratic governance of AI.

5.3 Regulatory approaches compared: Potential lessons for the UK?

The report authored by Sir Tony Blair and Lord Hague, cited in section 3.2 of this briefing, evaluated the differing regulatory approaches taken by the EU and US, and offered recommendations on how the UK’s own approach should proceed. It argued that both the EU and US approaches pose challenges that the UK should seek to diverge from over time.

For example, the report noted that representatives of the Large-scale Artificial Intelligence Open Network have written to the European Parliament warning that the EU’s draft AI Act and its “one-size-fits-all” approach will entrench large firms to the detriment of open-source developers, limit academic freedom and reduce competition. If the EU overly regulates AI, the report argued, it will repeat earlier failures with other technology families and become a less relevant global market due to declining growth rates.

Meanwhile, the report suggested that a “modern aversion” on the part of the US to investing directly in state capabilities could hamper its ability to lead on setting international standards and norms. It noted that, at the height of the space race, the US spent $42bn in today’s money on NASA funding in one year alone. By comparison, in 2022 the US spent $1.73bn on non-defence AI research and development, much of which was contracted out to industry and academic researchers. The report argued that, without sovereign-state capabilities, the US federal government could become overly reliant on private expertise and less able to set or enforce standards.

As a result, Sir Tony and Lord Hague contended that both the US and EU approaches risked locking in the current reality and leaders of AI, led by industry and lacking clear incentives for alignment with democratic control and governance.

They argued that the UK should aim to fill the niche of having a relatively less regulated AI ecosystem, but with a highly agile, technologically literate regulator tied closely to Sentinel, their proposed national AI laboratory, and its research in this space.

However, they noted that this approach will take time. The authors suggest that by combining flexible regulation with public investment in sovereign-state capacities, the UK can attract private AI start-ups while building the sovereign-state technical expertise required to set and enforce standards.

As a result, the report makes the following recommendations:

  • The UK should diverge from EU regulation on AI, but ensure its own regulatory systems allow UK companies and AI models to be assessed voluntarily at EU standards to enable exports.
  • In the near term, the UK should broadly align with US regulatory standards, while building a coalition of countries through Sentinel. This position may then diverge over time as UK regulatory expertise, the technology landscape and international approaches mature.
  • In the medium term, the UK should establish an AI regulator in tandem with Sentinel.

Cover image by Freepik.