This is a House of Commons Committee report, with recommendations to government. The Government has two months to respond.
Science, Innovation and Technology Committee
Governance of artificial intelligence (AI)
Date Published: 31 August 2023
This is the full report, read the report summary.
1. The rapid advancement of artificial intelligence (AI) has ushered in a new era of transformative technologies with far-reaching implications for society. As AI permeates various aspects of our lives, concerns regarding its governance and ethical considerations have become increasingly pertinent. This select committee report delves into the multifaceted landscape of AI governance, aiming to provide a comprehensive analysis of the existing frameworks, regulations, and ethical guidelines governing this powerful technology. By examining the benefits, risks, and potential social impacts, this report seeks to inform policymakers, stakeholders, and the public about the urgent need for a robust and transparent AI governance framework that upholds human rights, accountability, and societal well-being.
2. The above paragraph was authored not by the Science, Innovation and Technology Committee but by ChatGPT, using a simple prompt: write a 100-word introduction to a select committee report examining the governance of artificial intelligence. It captures some key themes that have emerged during our inquiry to date, and illustrates the fact that AI is now a general-purpose, ubiquitous technology—but, the above paragraph shows, not yet a perfect substitute for the way things are done now.
3. The recent rate of development has made debates regarding the governance and regulation of AI less theoretical, more significant, and more complex. We have therefore decided to publish an interim Report to outline our initial findings. Our inquiry continues and a further Report will be published in due course.
4. We launched our inquiry on 20 October 2022, to examine: the impact of AI on different areas of society and the economy; whether and how AI and its different uses should be regulated; and the UK Government’s AI governance proposals. We have received and published over 100 written submissions and taken oral evidence from 24 individuals, including AI researchers, businesses, civil society representatives, and individuals affected by this technology. We are grateful to everyone who has contributed to our inquiry so far.
5. This interim Report examines the factors behind recent AI developments, highlights the benefits offered by the technology, and identifies a series of challenges for policymakers. We examine how the UK Government has responded, and how this compares to other countries and jurisdictions.
6. Artificial intelligence (AI), a broad term with no universally agreed definition,1 has been discussed and debated since at least 1950, when Alan Turing posed a now-famous question: can machines think?2 In this Chapter, we will highlight notable recent breakthroughs, consider some of the forces that have propelled this rapid rate of development, and the implications.
7. AI development has focused increasingly on the ‘training’ and deployment of “… large, costly, wide-capability foundation models or general purpose AI systems (such as OpenAI’s GPT-3 or Google’s PaLM) which are then tailored to (or ‘finetuned’ for) particular tasks and application areas”.3 Terms such as foundation models, generative AI and large language models are often used interchangeably to refer to these models and tools, but they can broadly be defined as “… AI that is able to use tech in the place of a human being”.4
8. There is a growing number of these models and tools, but it is ChatGPT, launched in November 2022, that has sparked a global conversation. Hugh Milward, General Manager, Corporate, External and Legal Affairs at Microsoft UK, told us, shortly after Microsoft announced a “multibillion dollar” investment in ChatGPT developer OpenAI,5 that recent events had opened the door to “… a new industrial revolution”.6 The same analogy was also used by former UK Government Chief Scientific Adviser, Sir Patrick Vallance.7
9. Hugh Milward was among the contributors to our inquiry who pointed out that AI is not a new technology.8 In 1956, researchers convened at Dartmouth College, New Hampshire, to examine “… how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves”.9 Over the following decades, AI’s potential has been a focus for researchers, technology firms, and investors, as Jen Gennai, Director (Responsible Innovation) at Google, confirmed to us:
… it has had its ups and downs… it got unpopular for a while because people did not see where AI could help to solve some of the real problems and opportunities for economic development, commercial development or otherwise. We are now seeing more of that potential.10
10. Professor Michael Osborne, Professor of Machine Learning at the University of Oxford, said that the capabilities of AI models and tools remained limited, and described them as “… very far from human-level intelligence, and even ChatGPT and similar large language models… still have really significant gaps in their understanding of the complexities of the real world”.11
11. Professor Mihaela van der Schaar, Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, also suggested that the real world held challenges for models and tools capable of solving complex problems in more rigid environments such as board games: “… if we take something like the NHS, it is complex, messy data… the environment is changing according to rules we cannot really predict”.12
12. The ability to alter the performance of tools such as ChatGPT was also highlighted to us. Professor Sir Nigel Shadbolt, Professorial Research Fellow in Computer Science at the University of Oxford, described how “… ChatGPT is the thing you get in front of you, but there are lots of ways of getting into and behind those models and changing their behaviour, for sure”.13 AI models and tools can also ‘hallucinate’, offering incorrect answers,14 disseminating misinformation15 or revealing private data.16
13. The rate of development has nevertheless been notable and, as Sir Patrick Vallance told us, surprising:
I think everyone has been surprised by how much the large generative models have done things that people did not expect them to do. That is what is intriguing about it—very large datasets, very high compute power, and those models are turning out things that even people very close to the field thought, “Actually, I wasn’t sure it was going to do that”.17
14. Professor Osborne cautioned that “predicting the future is a mug’s game”, but also said that there had been “… repeated cases of the technology vastly exceeding what we had reasonably expected to be done” and developments that “… massively improved on what people thought would be possible in short order”.18
15. While AI is not a new technology, the rapidly acquired ubiquity of tools such as ChatGPT and the rate of development has come as a surprise to even well-informed observers. We are all now interacting with AI models and tools daily, and we are increasingly aware of these interactions.
16. Nevertheless, the technology should not be viewed as a form of magic or as something that creates sentient machines capable of self-improvement and independent decisions. It is akin to other technologies: humans instruct a model or tool and use the outputs to inform, assist or augment a range of activities.
17. The emergence of AI as a general-purpose, ubiquitous technology has affected many areas of society and the economy. In this Chapter we will consider the integration of AI models and tools into everyday devices, and the benefits it offers in two vital policy areas: medicine and healthcare, and education.
18. AI models and tools are already widely used in consumer products such as smartphones, satnavs, and streaming service recommendations.19 Recently companies such as Google and Microsoft have announced a series of integrations into new and existing products, such as Bard,20 Google Search,21 Bing,22 and Microsoft 365 Copilot—all with a view to increasing productivity.23
19. Our inquiry has coincided with a period of intense competition to deliver AI-centred announcements across different sectors. Adrian Joseph, Chief Data and AI Officer at BT Group told us that “… we have been in that race for a very long time. This is not new. The big tech companies have been acquiring start-ups and investing in their own expertise for 10, if not 20, years”.24 Below, we will consider two areas of society and the economy that have already benefited from the technology.
20. Medicine and healthcare is often said to be particularly well-placed to benefit from the use of AI models and tools—in the 2023 AI white paper, improvements in NHS medical care are listed among the key societal benefits.25 We have heard that AI is already delivering benefits and has further potential in two areas: healthcare provision and medical research.
21. AI can be used in healthcare as a diagnostic tool, capable of processing data and predicting patient risks.26 Dr Manish Patel, CEO of Jiva.ai, described how his firm developed an algorithm to recognise potential prostate cancer tissue from MRI scans.27 The Department of Health and Social Care has also invested in projects focused on using AI to detect other forms of cancer,28 and established an AI Diagnostic Fund “… to accelerate the deployment of the most promising AI imaging and decision support tools” across NHS Trusts.29
22. Dr Patel told us that a key advantage was the speed at which these tools could help medical professionals reach a diagnosis, avoiding longer waits and the associated emotional and financial costs.30 Professor Delmiro Fernandez-Reyes, Professor of Biomedical Computing at University College London, said that it could also help relieve pressure on medical personnel, by augmenting their work, speeding up referrals and preventing diseases from worsening.31
23. Dr Patel noted that there is “… a very high barrier to entry” for companies offering AI diagnostic tools to healthcare providers, owing to the need for sufficiently representative training datasets, and a robust regulatory framework intended to ensure such tools are deployed safely.32 Professor Mihaela van der Schaar of the University of Cambridge pointed to a longstanding trend of bias in medical data: “… at times the data we collect—not only at one point, but over time as interventions are made—is biased”.33 Dr Patel also said that the inherent bias within medical formulas such as the Body Mass Index34 could not be automatically out-programmed by such tools.35 Given this, he told us that the technology should be viewed as a way to augment rather than replace human expertise: “I don’t see that changing in the next 10 years. I think the medical community has to be confident that this technology works for them, and that takes time and it takes evidence”.36
24. AI models and tools can also deliver benefits via the automation of existing processes—“… doing the dirty work” of improving logistics, as Professor Mihaela van der Schaar of The University of Cambridge phrased it.37 She described how during the covid-19 pandemic tools were developed “… to predict how many beds and ventilators we would need and who would need them” and argued that pursuing similar efficiencies should be the primary use case for AI in medicine and healthcare.38
25. Professor Michael Osborne of the University of Oxford described AI models and tools as “… a way to automate away much of the tedious admin work that plagues frontline workers in the NHS today, particularly in primary healthcare”, and said that the technology could help medical professionals process letters and manage data.39
26. AI models and tools can transform healthcare provision, by assisting with diagnostics and, perhaps more significantly, freeing up time for the judgement of medical professionals by automating routine processes.
27. Our inquiry has also heard how AI models and tools can help deliver breakthroughs in medical research, such as drug discovery. Dr Andrew Hopkins, Chief Executive of Exscientia, a ‘pharmatech’ company,40 described to us how it used the technology to “… design the right drug and select the right patient for that drug”, and how this allowed for a complexity of analysis beyond the cognitive and computational capabilities of human researchers.41 He said that this analysis could be applied to new drugs and existing drugs that had previously failed to pass clinical trials on efficacy grounds, with a view to repurposing them.42
28. The global pharmaceutical company GSK described the impact of AI models and tools on medical research in similarly positive terms, and said that “… ultimately, AI will provide greater probability that the discovery and development of new medicines will be successful”.43
29. The ability of AI models and tools to process substantial volumes of data, and rapidly identify patterns where human researchers might take months or be unable to, makes it a potentially transformational technology for medical research. Either through the development of new drugs, or the repurposing of existing ones, the technology could reduce the investment required to bring a drug to market; and bring personalised medicine closer to becoming a reality.
30. Following the launch of ChatGPT in November 2022, its implications for education have been widely discussed, particularly its use by students. Joel Kenyon, a science teacher at a secondary school in London, told us that whilst he could not quantify the extent to which ChatGPT and other similar tools were being used by pupils, “… they are using it. There is no two ways about it”.44 Daisy Christodoulou, a former English teacher and current Director of Education at No More Marking, an education software provider, said that there was “… an uneven distribution. There are some teachers and some students who have started using it a lot, and there are some who have not heard of it. It is spreading”.45
31. An AI-accelerated shift towards personalised learning was also highlighted to us as a tangible benefit. Professor Rose Luckin, Professor of Learner Centred Design at University College London, said that there was evidence to show that “… students who might have been falling through the net can be helped to be brought back into the pack” with the help of personalised AI tutoring tools.46 The Prime Minister, the Rt. Hon. Rishi Sunak MP, told the Liaison Committee that “tutoring in the physical sense is hard to scale, but the technology allows us to provide that, and I think that would be transformational”.47
32. We also heard from Mr Kenyon about how AI tools were useful time-savers in everyday tasks undertaken by teachers.48 Ms Christodoulou said tools such as ChatGPT were particularly suited to certain tasks, such as text summarising: “If you take a complexish text where you would vouch for the accuracy—Wikipedia is a good example—pop it into ChatGPT and say, “Can you rewrite this so that it is appropriate for a 10- year-old?”, it does quite a good job”.49
33. We heard different perspectives on the potential longer-term implications. Daisy Christodoulou recommended “… a good, hard look at how we assess. I think that ChatGPT has huge implications for continuous assessment course work. It is very hard to see how that continues”. She also highlighted ChatGPT’s ability to generate “… university-level essays. The point is that even if it can produce them only at the 50 to 60 percentile, by definition that will be good enough for 50% to 60% of students”.50
34. Professor Luckin, however, was more positive. She argued that the emergence of AI, and its ability to compile information more efficiently than humans, had created an opportunity to move away from an information-based curriculum:
“… what are the real characteristics of human intelligence that we want our populations to have? Surely, they are not the ones that we can easily automate; surely, they are the ones that we cannot easily automate”.51
35. Dr Matthew Glanville, Head of Assessment Principles and Practice at the International Baccalaureate, said that the education qualification provider required its students to cite AI-generated content “… as they would reference any other material they have taken from a different source”.52 He said that AI would become “… part of our everyday lives… We really need to make sure that we support our students in understanding what ethical and useful approaches are”.53
36. AI tools are already useful time-savers for education professionals, and whilst reliable data is hard to come by, it seems highly likely that the technology is this generation of students’ calculator or smartphone.
37. The benefits for time-pressed teachers using AI models and tools to help prepare lesson plans are clear, and increased availability of personalised learning and tutoring tools could benefit many pupils. However, widespread use of AI raises questions about the nature of assessment, particularly in subjects that rely heavily on coursework.
38. Education policy must prioritise equipping children with the skills to succeed in a world where AI is ubiquitous: digital literacy and an ability to engage critically with the information provided by AI models and tools.
39. Our inquiry has highlighted potential future benefits of AI across different areas of society and economic activity. Climate change,54 antibiotic resistance,55 the transition to driverless vehicles,56 and the development of fossil fuel alternatives57 are some of the global challenges that we have heard could be addressed using AI models and tools.
40. The wide range of potential applications, and associated benefits, reflects the general-purpose nature of AI. As with previous technological innovations, the challenge for policymakers is translating this potential into reality, in a safe and sustainable way.
41. The rapid development and deployment of AI models and tools has led to intense interest in how public policy can and should respond to ensure that the beneficial consequences of AI can be reaped at the same time as the public interest is safeguarded and, specifically, potential harms to individuals and society are prevented. In the UK, the Government published in March 2023 a white paper outlining its “pro-innovation approach to AI regulation”.58 In the European Union a new legislative instrument, the Artificial Intelligence Act, has reached the final stage of development, negotiations between the Council of Ministers and the European Parliament.59 Proposals for legislative action have also been put forward by United States Senate Majority Leader, Chuck Schumer.60
42. In many jurisdictions—including our own—there is a sense that the pace of development of AI requires an urgent response from policymakers if the public interest is not to be outstripped by the pace of deployment. This is reinforced by the perception that the explosion of social media over the last 20 years took place before serious and coherent steps were taken to counteract harms—resulting in, for example, the Online Safety Bill still making its way through Parliament in 2023.61 By contrast, a more successful experience can be seen in the governance of other new fields of technology, such as the regulation of human fertilisation embryology in the UK following the Warnock Report in 1984.62
43. Holding back a coherent policy response to AI is the reality that the optimal responses to all of the challenges AI gives rise to are not always—at this stage—obvious. So there is a growing imperative to accelerate the development of public policy thinking on AI so that it is not left irretrievably behind by the pace of technological innovation.
44. In this Chapter, based on evidence that we have taken before our inquiry, we set out twelve challenges that the governance of AI must meet. We say that the UK Government in responding to this Report and to its own white paper must set out how it will address each of these challenges.
45. Researchers and developers are reliant on data to test, train, operate and refine AI models and tools.63 Professor Michael Osborne, a Professor of Machine Learning at the University of Oxford, told us that as datasets are compiled by humans, they contain inherent bias. He said that it was “… an illusion to think that data is neutral and objective”.64 Adrian Joseph, Chief Data and AI Officer at BT Group, said there was “… bias in the data, in the algorithms and in the individuals that are creating some of the algorithms”.65
46. The risks of encoding bias into AI models and tools are clear. Creative Commons, a non-profit organisation, said that if left unchecked it could “… replicate biases in society against minority and underrepresented communities, and lead to discrimination in critical areas affecting people’s lives… “.66 If the bias in datasets used to train AI models and tools is not accounted for and addressed, they “… will faithfully reproduce that bias”, Professor Osborne told us.67
47. Examples of where bias could have a particularly negative effect include recognition disparities between ethnic backgrounds by facial recognition tools used by law enforcement,68 employment tools that associate women’s names with traditionally female roles,69 the spread of politically-motivated disinformation and perpetuation of biased worldviews,70 and racial bias in insurance pricing.71
48. We have already outlined the reliance of AI models and tools on data, and some of the challenges this presents. A related privacy challenge also applies. Michael Birtwistle, Associate Director for AI and Data Law and Policy at the Ada Lovelace Institute, and a former UK Government official, told us that regardless of the sector, privacy should be “… an integral part of the balance of interests that you consider when you are deploying artificial intelligence”.72
49. A balance between the protection of privacy and the potential benefit of deploying AI models and tools is particularly important in certain use cases, such as law enforcement. Lindsey Chiswick, Director of Intelligence at the Metropolitan Police, told us that it currently used two facial recognition technology techniques using NEC software:
… the AI element of that is where the algorithm is doing that biometric matching. Essentially, it is looking at the watchlist—looking at each image and taking a set of measurements of the face; that is the biometric bit—and comparing it to the measurements of the face of the image that the cameras then pick up.73
50. Ms Chiswick said that facial recognition technology was deployed on the basis of specific intelligence and that images were compared against either a bespoke watchlist unique to a particular deployment (for live facial recognition, or LFR) or images on the Police National Database (in the case of retrospective facial recognition).74 She said that in 2023, the technology had been used six times, including during the coronation of King Charles III:
There were four true alerts. There were zero false alerts throughout those six deployments, and there have been two arrests made. The others were correct identifications but it was decided that arrest was not a necessary action in the circumstances.75
51. Dr Tony Mansfield, Principal Research Scientist at the National Physical Laboratory (NPL) described to us how an evaluation of the accuracy of the facial recognition software used by the Metropolitan Police, undertaken by the NPL,76 found that it could vary, depending on the ‘face-match threshold setting’ used:
We find that, if the system is run at low and easy thresholds, the system starts showing a bias against black males and females combined. There is some evidence for that—if the system is operated at certain thresholds, which I believe are outside those that the Met police has been deploying.77
Big Brother Watch, a campaigning organisation, has argued that the NPL study provided evidence of “serious demographic accuracy bias”, in the form of:
… a statistically significant difference between the false positive rate of black and non-black subjects… specifically when the confidence threshold to generate a match was set below 0.6… Documents seen by Big Brother Watch show that the Met Police has frequently operated LFR below a 0.6 confidence threshold and set the threshold as low as 0.55, in 2017 and 2018, while its LFR policy suggests that the threshold is variable.78
52. Whilst Ms Chiswick outlined the operational benefits, concerns were raised by other contributors to our inquiry. Big Brother Watch told us that whilst individual rights were protected by the Human Rights Act 1998, Data Protection Act 2018 and the Equality Act 2010, “… we have often found systems… which do not adequately respect the rights of individuals as set out by these pieces of legislation—for example, police forces’ use of live facial recognition”.79 Michael Birtwistle of the Ada Lovelace Institute told us that in the absence of a comprehensive regulatory framework “… there is not a guarantee that facial recognition systems deployed by police will meet reasonable standards of accuracy or that their use will remain proportionate to the risks presented by them”.80
53. Ms Chiswick emphasised that the decision to deploy this technology was not taken with “… a fishing expedition” in mind, and that the necessity and proportionality of each deployment was given careful consideration.81 She acknowledged public concerns around its use but confirmed that the Met was exploring other uses for AI to assist its operations.82
54. During recent years, controversies around ‘fake news’ have become increasingly frequent. The combination of data availability and new AI models and tools massively expands the opportunities for malign actors to ‘pass off’ content as being associated with particular individuals or organisations when it is in fact confected. Paul W. Fleming, General Secretary of the Equity trade union, described this process as:
… the creation of something completely new that never happened. It may be a series of static images that are then brought together into a video—an image of a politician slowly moved into saying something that they do not particularly want to say, or perhaps something that they do want to say.83
55. The use of image and voice recordings of individuals can lead to highly plausible material being generated which can purport to show an individual saying things that have no basis in fact.84 This material can be used to damage people’s reputations, and—in election campaigns—poses a significant threat to the conduct of democratic contests. Dr Steve Rolf, a researcher at the University of Sussex Business School, highlighted the potential for such material to impact “… democratic processes—for example, algorithmic recommendations on social media platforms that discourage wavering voters from turning out, thus tipping the balance in an election”.85
56. Other uses of faked content can lead to fraud. For example, voice recognition technology is used extensively to verify people’s identity by many financial services providers in telephone transactions.86 AI already makes it possible to reproduce a person’s voice patterns to speak words that they have never said, risking access through the voice recognition security barrier that has been imposed.87
57. Access to sufficient volumes of high-quality data is a priority for AI developers and researchers.88 Dr Andrew Hopkins, Chief Executive of Exscientia, told us that 40% of the drug discovery firm’s employees were “… experimental biologists in the laboratory, generating data. You need to have high-quality data to generate your algorithms”.89 The need for significant volumes of training data places developers with the most resources at an advantage. The Ada Lovelace Institute, a research institute, wrote that this explained the leading role of certain AI developers, thanks to their significant stores of data.90
58. The data access challenge also raises competition concerns. The UK Competition and Markets Authority is currently reviewing “… the likely implications of the development of AI foundation models for competition and consumer protection”91 whilst Lina Khan, Chair of the United States Federal Trade Commission, has emphasised the importance of fair competition in the AI sector.92 The Ada Lovelace Institute proposed legislation to us that would mandate research access to Big Tech data stores, to encourage a more diverse AI development ecosystem.93 Creative Commons also advocated for “… the creation of high quality, open data sets”.94
59. AI developers and researchers require access to sufficient levels of compute95 to power the development, training and refining of AI models and tools. Professor Sir Nigel Shadbolt, Professorial Research Fellow at the university of Oxford, said that the emergence of foundation models and generative AI could be partly attributed to an “… extraordinary increase” in compute availability.96
60. However, vast amounts of compute power is costly and therefore disproportionately available to the largest players. Professor Shadbolt told us that university researchers risked being left behind private developers as compute requirements continued to grow.97 The UK Government has announced plans to establish an Exascale supercomputer facility and an AI-dedicated compute resource to support research.98 The Prime Minister, Rt. Hon. Rishi Sunak MP, has also confirmed that three AI labs—Google DeepMind, OpenAI and Anthropic—will “… give early or priority access to models for research and safety purposes”.99
61. As increased availability of data and compute have facilitated the development of new AI models and tools, these have increasingly become ‘black boxes’, that is, their decision-making processes are not explainable. The challenge, as put to us by Professor Michael Osborne of The University of Oxford, is “… to what degree an AI should be able to explain itself”.100 The challenge is further complicated by the fact that the better an AI model or tool performs, the less explainable it is likely to be.101 Adrian Joseph of BT Group drew parallels with the human brain and described it as “the ultimate black box”.102
62. When asked whether the AI models and tools used by Exscientia were inherently black boxes, Chief Executive, Dr Andrew Hopkins, said that they were not, and that knowing the “provenance” of outputs was valuable: “… connecting the dots between a prediction and the data that led to a prediction is vital for understanding in science, as much as it is for the general public”.103
63. Professor Sir Nigel Shadbolt described the suggestion that future AI models and tools would inevitably be black boxes as “… a counsel of despair” and said that policymakers and wider society should “… demand that these systems begin to render some of the processes by which they came up with the output they do more transparent and more explicable”.104
64. Greater explainability would also, according to the Public Law Project, help increase public trust in the deployment of AI models and tools in different sectors.105 However, researchers and medical professionals at the Birmingham AI and Digital Healthcare Group, pointed out that other aspects of medicine were “… relatively black box” and that “… whilst greater explainability for all health interventions is desirable, it should not be an absolute requirement” if they are proven to be safe and effective.106
65. There are different views on whether the code used to run AI models and tools should be freely available—or open-source—for testing, scrutiny and improvement. Proponents of this approach have said that it can encourage innovation and prevent monopolisation by powerful, well-resourced players. The Tony Blair Institute for Global Change has pointed out that open-source innovation “underpins the entire Internet”.107
66. Creative Commons told us an open-source approach can lower barriers to development, and that “… the capacity to develop and use AI… should be widely distributed, rather than concentrated among a narrow few”.108 Professor Mihaela van der Schaar of the University of Cambridge said that, in settings where sensitive personal data are used, such as medicine and healthcare, the relevant authorities should encourage the use of open-source platforms that are open to inspection and robustly tested for safety.109
67. However, others have highlighted the value of keeping code proprietary, and argued that doing so can also help protect against misuse, for example through the dissemination of misleading content.110 “The competitive landscape and safety implications” were cited by OpenAI (which, paradoxically, has not made its latest models and tools open-source) as reasons to limit public disclosure of information about “… the architecture (including model size), hardware, training compute, dataset construction, training method, or similar” of its most advanced model, GPT-4.111 Chief Scientist Ilya Sutskever cited the potential for malign actors to cause harm using open-source models:
These models are very potent and they’re becoming more and more potent. At some point it will be quite easy, if one wanted, to cause a great deal of harm with those models. And as the capabilities get higher it makes sense that you don’t want to disclose them.112
68. Whilst the use of AI models and tools have helped create revenue for the entertainment industry in areas such as video games and audience analytics,113 concerns have been raised about the ‘scraping’ of copyrighted content from online sources without permission.114 Jamie Njoku-Goodwin, CEO of UK Music, told us that his industry operated on the “… basic principle that, if you are using someone else’s work, you need permission for that and must observe and respect the copyright”,115 but that new tools allowed this to be circumvented. He described the process as:
… people taking the work of creators, which has a copyright attached, feeding it into an AI, using that to generate so-called new works and then, potentially, being able to monetise off the back of that, without respecting or recognising the inputs.116
Ongoing legal cases are likely to set precedents in this area.117
69. Representatives of the creative industries told us that they hoped to reach a mutually beneficial solution with the AI sector, potentially in the form of a licensing framework for the use of copyrighted content to train models and tools.118 Jamie Njoku-Goodwin said that he and UK Music’s members would welcome greater engagement and that he had “… not had any evidence of companies coming to us to say, “We would like to seek a licence to use works to train AI”.119 Dr Hayleigh Bosher, an intellectual property researcher at Brunel University, pointed out that AI and tech firms would also benefit from the enforcement of copyright and intellectual property rights.120
70. The Intellectual Property Office, an executive agency of the UK Government, has begun to develop a voluntary code of practice on copyright and AI, in consultation with the technology, creative and research sectors.121 It has said that the guidance should “… support AI firms to access copyrighted work as an input to their models, whilst ensuring there are protections (e.g. labelling) on generated output to support right holders of copyrighted work”. A draft code is expected to be published by the end of July.122 The Government has said that if agreement is not reached or the code not adopted, it may legislate.123
71. In February 2023, following criticism from the creative industries,124 the Government withdrew a proposed text and database mining exception for any purpose, such as the development of AI models and tools. An exception for the purposes of non-commercial research has been in place since 2014.125 These competing incentives define the intellectual property and copyright challenge. The Library and Archives Copyright Alliance told us that the decision to withdraw the proposed exception “… prevents the UK from capitalising on the diverse, agile and creative benefits that AI can bring to the UK’s economy, its society and its competitive research environment”.126
72. A trend towards increasingly complex and international supply chains for AI models and tools, involving “… cloud-based services, servers, protocols, data centres, third-party data sources, and content delivery networks”,127 has created a challenge over the determination of liability for unsafe or harmful uses of the technology, and compliance with governance requirements to mitigate risk.
73. The Trades Union Congress suggested to us that “… different actors influencing the technology at development, procurement and application” stages made identifying responsibility for discriminatory uses of AI models and tools challenging.128 At deployment stage, the Ada Lovelace Institute said that obligations should rest on developers, providers (including intermediaries) and end users.129
74. Developers also expressed support for distributed liability. DeepMind, since merged with the Google Research Brain Unit to form Google DeepMind,130 said that responsibility should be spread across supply chains “… based on which aspect is most likely to lead to harm and an actor’s practical ability to comply given the structure of the market and the nature of their contribution”.131
75. Sir Patrick Vallance, former Chief Scientific Adviser to the UK Government, said to us that the increasing ubiquity of AI models and tools will have “… a big impact on jobs, and that impact could be as big as the industrial revolution was”.132 Professor Michael Osborne of the University of Oxford said that he expected “… tasks that involve routine, repetitive labour and revolve on low-level decision-making to be automated very quickly”.133
76. Our inquiry heard different perspectives on automation. Paul W. Fleming, General Secretary of the Equity trade union, said that whilst some opportunities to earn were being reduced and jobs were being displaced, the technology’s rise offered: “… a whole new frontier of income potential and new work for our members. This is not something to be frightened or worried about”.134 Hugh Milward of Microsoft UK was also not worried, and viewed AI as a “… co-pilot, not an autopilot. Its job is to augment the things that human beings are doing, rather than to replace the things that human beings are doing, and to really allow humans to be more human, in some respects”.135
77. When assessing the potential impact of automation across the economy and society, Sir Patrick told us that it would be important to plan ahead, and ask:
… Which are the jobs and sectors that will be most affected, and what are the plans to retrain, or give people their time back to do a job differently? There will be jobs that can be done by AI, which can either mean that lots of people do not have a job or that, actually, lots of people have a job that only humans can do.136
The Prime Minister, Rt. Hon. Rishi Sunak MP, has highlighted the socioeconomic risks created by “large-scale societal shifts” associated with the development of AI:
That does not mean that you should stand in the way of it, but it just means we should make sure that we are cognisant of it and provide people with the skills they need to flourish in a world that is being changed by technology.137
78. Different jurisdictions have proposed different approaches to AI governance. The UK Government has expressed a preference for a “pro-innovation approach” in the AI white paper, published in March 2023.138 The draft Artificial Intelligence Act currently being negotiated between European Union Member States and the European Parliament would implement a risk-based approach, with AI models and tools grouped into risk categories and some, such as biometric surveillance, emotion recognition and predictive policing, banned altogether.139 In the United States, the White House has called on AI developers “… to take action to ensure responsible innovation and appropriate safeguards, and protect people’s rights and safety”,140 whilst Senate Majority Leader Chuck Schumer has said that a proposed legislative approach will be outlined in the autumn of 2023.141
79. Whilst there are divergent approaches currently being pursued, our inquiry has heard that the global implications of AI’s emergence as a ubiquitous, general-purpose technology demand a coordinated response. As Professor Michael Osborne of the University of Oxford told us, internationally agreed and harmonised principles to inform AI governance were desirable “… because the problems that we face are very similar”.142
80. There are various international fora where such discussions could take place, and the development of a coordinated response will be shaped by geopolitical considerations. The Prime Minister, Rt. Hon. Rishi Sunak MP, has taken steps to position the UK as a leading actor in these discussions by announcing that it would host a global summit on AI safety later in 2023.143 We nevertheless heard that the European Union and United States are likely to become the de facto global standard-setters in AI governance.144
81. We will further explore the UK’s regulatory approach to AI, international comparators, and initiatives to establish international coordination, in Chapters 5 and 6 of this interim Report.
82. A related but separate challenge concerns the international security implications of AI’s increasing prevalence, and debates over existential risks. Ian Hogarth, an investor and Chair of the UK Government’s AI Foundation Model Taskforce,145 has said that whilst it is difficult to predict when it will emerge, so-called “… God-like AI could be a force beyond our control or understanding, and one that could usher in the obsolescence or destruction of the human race”.146 Matt Clifford, an adviser to the UK Government on AI, has said that such a prospect would soon be realistic: “you can have really very dangerous threats to humans that could kill many humans, not all humans, simply from where we’d expect models to be in two years’ time”.147
83. In a joint submission, Dr Jess Whittlestone of the Centre for Long-Term Resilience think-tank and Richard Moulange, a researcher at The University of Cambridge, suggested scenarios where the use of AI models and tools could threaten or undermine national and/or international security. These included the development of novel biological or chemical weapons, the risk of unintended escalation and the undermining of nuclear deterrence.148
84. Appearing at the House of Lords Artificial Intelligence in Weapons Systems Committee in June 2023, former National Security Adviser, Lord Sedwill, said that whilst he did not expect AI to have the same impact on military doctrine as the development of nuclear weapons and subsequent development of the mutually assured destruction consensus,149 it nevertheless represented “… the future of defence capability and the UK needs to be at the forefront of that”.150
85. Whilst mutually assured destruction is widely accepted as valid military doctrine, there is disagreement as to whether the existential risks of AI highlighted by researchers and developers in May 2023 are realistic.151 Professor Michael Osborne said that he believed that such predictions could come to pass but suggested that the development of an international security framework to govern the development and use of nuclear weapons offered a template for mitigating the collective risks posed by AI.152
86. Professor Osborne’s co-researcher Michael Cohen said that if a shared understanding of the risks could be reached through international fora, then:
“… the game theory isn’t that complicated. Imagine that there was a button on Mars labelled ‘geopolitical dominance’, but actually, if you pressed it, it killed everyone. If everyone understands that, there is no space race for it”.153
It has been observed that reaching such a shared understanding and inspection framework comparable to those which govern the use of biological, chemical and nuclear weapons would present a significant diplomatic and technical challenge.154
87. The 2023 AI white paper, discussed in more detail in Chapter 5, described the “existential risks” posed by AGI as “high impact but low probability”.155 Joelle Pineau, vice-president of AI research at Meta, has warned against a focus on AGI, as it reduces the opportunity for “… rational discussions about any other outcomes. And that takes the oxygen out of the room for any other discussion, which I think is too bad”.156 MIT Technology Review editor Will Douglas Heaven has suggested that “if something sounds like bad science fiction, maybe it is”.157
88. The Government’s approach to AI governance and regulation should address each of the twelve challenges we have outlined, both through domestic policy and international engagement.
89. In March 2023, the UK Government set out its proposed “pro-innovation approach to AI regulation” in the form of a white paper,158 and the Department for Science, Innovation and Technology (DSIT) is currently evaluating responses to an accompanying consultation.159 In this Chapter we will examine the UK Government’s proposed approach to AI governance and regulation.
90. The white paper aimed to ensure “… that regulatory measures are proportionate to context and outcomes, by focusing on the use of AI rather than the technology itself”.160 It set out five principles to frame regulatory activity, guide future development of AI models and tools, and their use:
91. The white paper said that these principles would not initially be put on a statutory footing but interpreted and translated into action by individual sectoral regulators, with assistance from central support functions, initially delivered from within Government. Six proposed functions would cover:
92. The white paper outlined a series of deliverables on six-, twelve-, and twelve-month-plus timetables. Key deliverables included:
93. The white paper confirmed that whilst the Government did not intend to introduce AI-specific legislation immediately: “… when parliamentary time allows, we anticipate introducing a statutory duty on regulators requiring them to have due regard to the principles”.164
94. Contributors to our inquiry told us that the proposals in the white paper constituted a distinct, UK-specific approach to AI governance. Coran Darling, an Associate at law firm DLA Piper, described it as “a non-linear approach” that would provide “… the power and flexibility to regulators to distinguish their best practices and approach it on that basis”.165 Michael Birtwistle, Associate Director of the Ada Lovelace Institute and a former UK Government official, said that there were “… a lot of advantages to the context-specific approach”, but that implementation of the principles would be the key challenge.166
95. We also heard supportive sentiments from industry. Jen Gennai, Director (Responsible Innovation) at Google, told us that she favoured “… a principles-based approach that allows for support of innovation while ensuring guardrails”,167 whilst Hugh Milward, General Manager of Corporate, External and Legal Affairs at Microsoft UK, said that the development of AI governance principles that could be applicable irrespective of how the technology evolved would put the UK “… in a really good place to get that generation of innovation at the same time as guiding society”.168 Google’s submission to our inquiry emphasised that AI “… is far too important not to regulate and too important not to regulate well”.169
96. Asked whether the development of a UK-specific approach to AI governance would require primary legislation, the Prime Minister, Rt. Hon. Rishi Sunak MP, told the Liaison Committee:
I think what we need to do—and I think we can probably do lots of this without legislation—is sit down and figure out what safety features and guard rails we would like to put in place… it is too early to pre-empt what all that might look like, but you can imagine a world where at least the initial stages of that don’t require legislation, necessarily, but just require us to get in there and do safety evaluation on the models and have access to them.170
We will consider the European Union and United States’ respective approaches to AI governance in Chapter 6 of this interim Report.
97. The AI white paper highlighted the UK’s “… high-quality regulators and our strong approach to the rule of law, supported by our technology-neutral legislation and regulations” as a key factor in the development of its AI sector.171 It said that whilst some regulators were sufficiently resourced to respond to the development of AI models and tools and that some mechanisms for regulatory cooperation are already in place, others had “… limited capacity and access to AI expertise. This creates the risk of inconsistent enforcement across regulators”.172
98. The importance of regulatory capacity and coordination were highlighted by contributors to our inquiry, and the work of the Digital Regulation Cooperation Forum (DRCF), which brings together the Competition and Markets Authority, Information Commissioner’s Office, Ofcom and Financial Conduct Authority,173 was highlighted as an example of best practice.174 Katherine Holden of techUK, a trade body, suggested the establishment of “… formalised structures to co-ordinate approaches between regulators… an expanded version of the Digital Regulation Cooperation Forum [DRCF]”.175 RELX, an information and analytics company, also said that “regulators should be expected to engage with one another via the DCRF, and this should apply to all regulators that are likely to be involved in issuing guidance or rules on AI”.176
99. We heard that the rate of development in the field has created challenges for even the best-equipped regulators. Professor Sir Nigel Shadbolt, Professorial Research Fellow in Computer Science at the University of Oxford, said regulators were “… trying to gear up for this world of AI… trying to understand what technical skills they need and what they would need to put into the process to enable their job to be easier”.177 Michael Birtwistle of the Ada Lovelace Institute told us that ensuring properly-equipped regulators to the UK’s AI eventual governance framework would have a positive impact on public trust in AI models and tools: “… the importance and benefits of these technologies could be huge… we need public trust; we need those technologies to be trustworthy, and that is worth investing regulatory capability in”.178
100. In addition to the AI white paper, the UK Government has announced the formation of an AI Foundation Model Taskforce, chaired by investor Ian Hogarth.179 Bringing together experts from Government, industry and academia in a similar way to the successful covid-19 Vaccines Taskforce, it will have a mandate to “… carry out research on AI safety and inform broader work on the development of international guardrails, such as shared safety and security standards and infrastructure”.180
101. The Taskforce will invest an initial £100 million “… in foundation model infrastructure and public service procurement, to create opportunities for domestic innovation”. Pilots involving the use of AI models and tools in public services are expected to launch later this year.181
102. The UK has a long history of technological innovation and regulatory expertise, which can help it forge a distinctive regulatory path on AI. The AI white paper should be welcomed as an initial effort to engage with a complex task. However, the approach outlined is already risking falling behind the pace of development of AI.
103. The UK Government’s proposed approach to AI governance relies heavily on our existing regulatory system, and the promised central support functions. The time required to establish new regulatory bodies means that adopting a sectoral approach, at least initially, is a sensible starting point. We have heard that many regulators are already actively engaged with the implications of AI for their respective remits, both individually and through initiatives such as the Digital Regulation Cooperation Forum. However, it is already clear that the resolution of all of the Challenges set out in this report may require a more well-developed central coordinating function.
104. The AI white paper is right to highlight the importance of regulatory capacity to the successful implementation of its principles. The Government should, as part of its implementation of its proposals, undertake a gap analysis of the UK’s regulators, which considers not only resourcing and capacity, but whether any regulators require new powers to implement and enforce the principles outlined in the AI white paper.
105. The Government is yet to confirm whether AI-specific legislation will be included in the upcoming King’s Speech in November. This new session of Parliament will be the last opportunity before the General Election for the UK to legislate on the governance of AI. Following the Election it is unlikely that new legislation could be enacted until late 2025—more than two years from now and nearly three years from the publication of the white paper.
106. The Government has said in the AI white paper that it may legislate, at a minimum, to establish ‘due regard’ duties for existing regulators. That commitment alone—in addition to any further requirements that may emerge—suggests that there should be a tightly-focussed AI Bill in the new session of Parliament. Our view is that this would help, not hinder, the Prime Minister’s ambition to position the UK as an AI governance leader. We see a danger that if the UK does not bring in any new statutory regulation for three years it risks the Government’s good intentions being left behind by other legislation—like the EU AI Act—that could become the de facto standard and be hard to displace.
107. In its reply to this interim Report, and its response to the AI white paper consultation, the Government should confirm whether AI-specific legislation, such as the introduction of a requirement for regulators to pay due regard to the AI white paper principles, will be introduced in the next Parliament. It should also confirm what work has been undertaken across Government to explore the possible contents of such a Bill.
108. We welcome the establishment of a Foundation Model Taskforce, the appointment of Ian Hogarth as its chair, and the Government’s stated intention for it to take a similar approach to the Vaccines Taskforce. This agile approach is necessary and proportionate to the importance of the issue. The Government should confirm the Task Force’s full membership, terms of reference, and the first tranche of public sector pilot projects, in its reply to this interim Report.
109. In Chapter 4 of this interim Report, we identified twelve challenges that the governance of AI must address. Many of these challenges are international in nature, and the importance of international coordination of AI governance and regulation were highlighted by contributors to our inquiry. In this Chapter, we will consider the governance approaches in the European Union (EU) and United States, and the role of the UK in international coordination initiatives.
110. The European Union (EU) is one of two jurisdictions suggested to us as likely to play a leading role in shaping international AI governance and regulation, through an eventual EU AI Act proposed by the European Commission in 2021.182 Negotiations between the Commission, European Parliament and Member States over the final text of the Act are ongoing.183
111. In contrast to the UK’s proposed context-specific, principles-based approach, the draft EU AI Act takes a risk-based approach, with “… obligations for providers and those deploying AI systems depending on the level of risk the AI can generate”.184 It aims to set out a “technology-neutral, uniform definition of AI” and proposes grouping models and tools into risk categories, including unacceptable risk (which would be banned), high risk, and limited risk, with transparency obligations placed on generative AI tools, such as ChatGPT.185 It has been reported that the final Act is unlikely to be agreed until later in 2023, and may not be fully in force until 2026.186
112. Hugh Milward of Microsoft UK told us that the EU’s proposed approach to AI governance provided “… a model of how not to do it”,187 whilst Katherine Holden of techUK, a trade body, also expressed reservations, and described it as “… a very centralised approach… it does not allow the opportunity for much flexibility. It is not particularly future-proofed, because they have a static list of high-risk applications”.188 In an open letter published in June 2023, executives from 150 businesses said that the EU’s proposed approach would:
… jeopardise Europe’s competitiveness and technological sovereignty without effectively tackling the challenges we are and will be facing. This is especially true regarding generative AI. Under the version recently adopted by the European Parliament, foundation models, regardless of their use cases, would be heavily regulated, and companies developing and implementing such systems would face disproportionate compliance costs and disproportionate liability risks.189
113. In October 2022, the White House Office of Science and Technology Policy published a non-binding ‘Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People’.190 It offered “… a set of five principles and associated practices to help guide the design, use and deployment of automated systems to protect the rights of the American public”.191 The five principles were:
114. In May 2023, the White House called on AI developers “… to take action to ensure responsible innovation and appropriate safeguards, and protect people’s rights and safety”, following a meeting with leaders from four developers—Anthropic, Alphabet, Microsoft and OpenAI.193 Senate Majority Leader Chuck Schumer has since outlined a ‘SAFE Innovation Framework for AI Policy’ and said that “… a new legislative approach for translating this framework into legislative action” will begin in the autumn of this year.194
115. In an appearance at a subcommittee of the Senate Committee on the Judiciary examining privacy, technology and the law, OpenAI chief executive Sam Altman said that it was “… essential to develop regulations that incentivize AI safety while ensuring that people are able to access the technology’s many benefits”.195
116. Contributors to our inquiry highlighted the geopolitical context of AI development. Adrian Joseph, Chief Data and AI Officer at BT Group, said that “… there is a risk that we in the UK lose out to the large tech companies, and possibly China, and are left behind”.196 Microsoft’s Hugh Milward cited AI development as “… one example of an area where we have to stay ahead in ‘the West’ with values that are effectively pro-democracy”, when compared with states such as China.197
117. Following a visit to the United States in June, Prime Minister Rishi Sunak MP announced that the UK would host a global summit on AI safety: “[it] will bring together key countries, leading tech companies and researchers to agree safety measures to evaluate and monitor the most significant risks from AI”.198 Asked whether he intended to use the summit as a platform to establish an organisation comparable to the International Atomic Energy Agency, which includes a broad range of countries, including China and Russia,199 he said:
… AI does not respect national borders, and I think we will all benefit from hearing and talking to each other in a conversation with the businesses themselves. That is really what this is about. We are a long way from anyone establishing an IAEA equivalent for AI. Those things are long into the distance, but in the first instance, just talking this through with like-minded countries seems a sensible step.200
118. Google told us that it supported Government plans “… to actively take a role in shaping global norms”.201 We also heard that the UK could play a convening role and “… influence the global discussion on AI regulation in a way which is more compatible with the UK’s approach, delivers high standards and protections, and allows the UK to act as a bridge between different systems”.202
119. The Prime Minister was right to say that AI does not respect national borders, and we welcome the announcement of a global summit on AI safety in London. The challenges highlighted in our interim Report should form the basis for these important international discussions.
120. The summit should aim to advance a shared international understanding of the challenges of AI—as well as its opportunities. Invitations to the summit should therefore be extended to as wide a range of countries as possible. Given the importance of AI to our national security there should also be a forum established for like-minded countries who share liberal, democratic values, to be able to develop an enhanced mutual protection against those actors—state and otherwise—who are enemies of these values.
121. There is as little consensus about how AI will evolve as there has been excitement and hyperbole following its rise to ubiquity. AI cannot be un-invented. It has and will continue to change the way we live our lives. Humans must take measures to safely harness the benefits of the technology and encourage future innovations, whilst providing credible protection against harm.
122. Some observers have called for the development of certain types of AI models and tools to be paused, allowing global regulatory and governance frameworks to catch up. We are unconvinced that such a pause is deliverable. When AI leaders say that new regulation is essential, their calls cannot responsibly be ignored—although it should also be remembered that is not unknown for those who have secured an advantageous position to seek to defend it against market insurgents through regulation.
123. The twelve Challenges of AI Governance which we have set out must be addressed by policymakers in all jurisdictions. Different administrations may choose different ways to do this.
124. We believe that the UK’s depth of expertise in AI and the disciplines which contribute to it—the vibrant and competitive developer and content industry that the UK is home to; and the UK’s longstanding reputation for developing trustworthy and innovative regulation—provides a major opportunity for the UK to be one of the go-to places in the world for the development and deployment of AI. But that opportunity is time-limited. Without a serious, rapid and effective effort to establish the right governance frameworks—and to ensure a leading role in international initiatives—other jurisdictions will steal a march and the frameworks that they lay down may become the default even if they are less effective than what the UK can offer. We urge the Government to accelerate, not to pause, the establishment of a governance regime for AI, including whatever statutory measures as may be needed.
1. While AI is not a new technology, the rapidly acquired ubiquity of tools such as ChatGPT and the rate of development has come as a surprise to even well-informed observers. We are all now interacting with AI models and tools daily, and we are increasingly aware of these interactions. (Paragraph 15)
2. Nevertheless, the technology should not be viewed as a form of magic or as something that creates sentient machines capable of self-improvement and independent decisions. It is akin to other technologies: humans instruct a model or tool and use the outputs to inform, assist or augment a range of activities. (Paragraph 16)
3. AI models and tools can transform healthcare provision, by assisting with diagnostics and, perhaps more significantly, freeing up time for the judgement of medical professionals by automating routine processes. (Paragraph 26)
4. The ability of AI models and tools to process substantial volumes of data, and rapidly identify patterns where human researchers might take months or be unable to, makes it a potentially transformational technology for medical research. Either through the development of new drugs, or the repurposing of existing ones, the technology could reduce the investment required to bring a drug to market; and bring personalised medicine closer to becoming a reality. (Paragraph 29)
5. AI tools are already useful time-savers for education professionals, and whilst reliable data is hard to come by, it seems highly likely that the technology is this generation of students’ calculator or smartphone. (Paragraph 36)
6. The benefits for time-pressed teachers using AI models and tools to help prepare lesson plans are clear, and increased availability of personalised learning and tutoring tools could benefit many pupils. However, widespread use of AI raises questions about the nature of assessment, particularly in subjects that rely heavily on coursework. (Paragraph 37)
7. Education policy must prioritise equipping children with the skills to succeed in a world where AI is ubiquitous: digital literacy and an ability to engage critically with the information provided by AI models and tools. (Paragraph 38)
8. The wide range of potential applications, and associated benefits, reflects the general-purpose nature of AI. As with previous technological innovations, the challenge for policymakers is translating this potential into reality, in a safe and sustainable way. (Paragraph 40)
9. The Government’s approach to AI governance and regulation should address each of the twelve challenges we have outlined, both through domestic policy and international engagement. (Paragraph 88)
10. The UK has a long history of technological innovation and regulatory expertise, which can help it forge a distinctive regulatory path on AI. The AI white paper should be welcomed as an initial effort to engage with a complex task. However, the approach outlined is already risking falling behind the pace of development of AI. (Paragraph 102)
11. The UK Government’s proposed approach to AI governance relies heavily on our existing regulatory system, and the promised central support functions. The time required to establish new regulatory bodies means that adopting a sectoral approach, at least initially, is a sensible starting point. We have heard that many regulators are already actively engaged with the implications of AI for their respective remits, both individually and through initiatives such as the Digital Regulation Cooperation Forum. However, it is already clear that the resolution of all of the Challenges set out in this report may require a more well-developed central coordinating function. (Paragraph 103)
12. The AI white paper is right to highlight the importance of regulatory capacity to the successful implementation of its principles. The Government should, as part of its implementation of its proposals, undertake a gap analysis of the UK’s regulators, which considers not only resourcing and capacity, but whether any regulators require new powers to implement and enforce the principles outlined in the AI white paper. (Paragraph 104)
13. The Government is yet to confirm whether AI-specific legislation will be included in the upcoming King’s Speech in November. This new session of Parliament will be the last opportunity before the General Election for the UK to legislate on the governance of AI. Following the Election it is unlikely that new legislation could be enacted until late 2025—more than two years from now and nearly three years from the publication of the white paper. (Paragraph 105)
14. The Government has said in the AI white paper that it may legislate, at a minimum, to establish ‘due regard’ duties for existing regulators. That commitment alone—in addition to any further requirements that may emerge—suggests that there should be a tightly-focussed AI Bill in the new session of Parliament. Our view is that this would help, not hinder, the Prime Minister’s ambition to position the UK as an AI governance leader. We see a danger that if the UK does not bring in any new statutory regulation for three years it risks the Government’s good intentions being left behind by other legislation—like the EU AI Act—that could become the de facto standard and be hard to displace. (Paragraph 106)
15. In its reply to this interim Report, and its response to the AI white paper consultation, the Government should confirm whether AI-specific legislation, such as the introduction of a requirement for regulators to pay due regard to the AI white paper principles, will be introduced in the next Parliament. It should also confirm what work has been undertaken across Government to explore the possible contents of such a Bill. (Paragraph 107)
16. We welcome the establishment of a Foundation Model Taskforce, the appointment of Ian Hogarth as its chair, and the Government’s stated intention for it to take a similar approach to the Vaccines Taskforce. This agile approach is necessary and proportionate to the importance of the issue. The Government should confirm the Task Force’s full membership, terms of reference, and the first tranche of public sector pilot projects, in its reply to this interim Report. (Paragraph 108)
17. The Prime Minister was right to say that AI does not respect national borders, and we welcome the announcement of a global summit on AI safety in London. The challenges highlighted in our interim Report should form the basis for these important international discussions. (Paragraph 119)
18. The summit should aim to advance a shared international understanding of the challenges of AI—as well as its opportunities. Invitations to the summit should therefore be extended to as wide a range of countries as possible. Given the importance of AI to our national security there should also be a forum established for like-minded countries who share liberal, democratic values, to be able to develop an enhanced mutual protection against those actors—state and otherwise—who are enemies of these values. (Paragraph 120)
19. There is as little consensus about how AI will evolve as there has been excitement and hyperbole following its rise to ubiquity. AI cannot be un-invented. It has and will continue to change the way we live our lives. Humans must take measures to safely harness the benefits of the technology and encourage future innovations, whilst providing credible protection against harm. (Paragraph 121)
20. Some observers have called for the development of certain types of AI models and tools to be paused, allowing global regulatory and governance frameworks to catch up. We are unconvinced that such a pause is deliverable. When AI leaders say that new regulation is essential, their calls cannot responsibly be ignored—although it should also be remembered that is not unknown for those who have secured an advantageous position to seek to defend it against market insurgents through regulation. (Paragraph 122)
21. The twelve Challenges of AI Governance which we have set out must be addressed by policymakers in all jurisdictions. Different administrations may choose different ways to do this. (Paragraph 123)
22. We believe that the UK’s depth of expertise in AI and the disciplines which contribute to it—the vibrant and competitive developer and content industry that the UK is home to; and the UK’s longstanding reputation for developing trustworthy and innovative regulation—provides a major opportunity for the UK to be one of the go-to places in the world for the development and deployment of AI. But that opportunity is time-limited. Without a serious, rapid and effective effort to establish the right governance frameworks—and to ensure a leading role in international initiatives—other jurisdictions will steal a march and the frameworks that they lay down may become the default even if they are less effective than what the UK can offer. We urge the Government to accelerate, not to pause, the establishment of a governance regime for AI, including whatever statutory measures as may be needed. (Paragraph 124)
Greg Clark, in the Chair
Dawn Butler
Chris Clarkson
Tracey Crouch
Katherine Fletcher
Rebecca Long-Bailey
Stephen Metcalfe
Graham Stringer
Draft Report (Governance of artificial intelligence: interim Report), proposed by the Chair, brought up and read.
Ordered, That the draft Report be read a second time, paragraph by paragraph.
Paragraphs 1 to 124 read and agreed to.
Summary agreed to.
Resolved, That the Report be the Ninth Report of the Committee to the House.
Ordered, That the Chair make the Report to the House.
Ordered, That embargoed copies of the Report be made available, in accordance with the provisions of Standing Order No. 134.
Adjourned till Wednesday 6 September 2023 at 9.20am.
The following witnesses gave evidence. Transcripts can be viewed on the inquiry publications page of the Committee’s website.
Professor Michael Osborne, Professor of Machine Learning and co-founder, University of Oxford and Mind Foundry; Michael Cohen, DPhil candidate in Engineering Science, University of OxfordQ1–54
Mrs Katherine Holden, Head of Data Analytics, AI and Digital Identity, techUK; Dr Manish Patel, CEO, Jiva.aiQ55–96
Adrian Joseph, Chief Data and AI Officer, BT Group; Jen Gennai, Director, Responsible Innovation, Google; Hugh Milward, General Manager, Corporate, External and Legal Affairs, Microsoft UKQ97–143
Professor Dame Wendy Hall, Regius Professor of Computer Science, University of Southampton; Professor Sir Nigel Shadbolt, Professorial Research Fellow in Computer Science and Principal, Jesus College, University of OxfordQ144–173
Professor Andrew Hopkins, Chief Executive, ExscientiaQ174–222
Professor Delmiro Fernandez-Reyes, Professor of Biomedical Computing, University College London, Adjunct Professor of Paediatrics, University of Ibadjan; Professor Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, and Director, Cambridge Centre for AI in Medicine, The University of CambridgeQ223–262
Professor Rose Luckin, Professor of Learner Centred Design, University College London, Director, Educate; Daisy Christodoulou, Director of Education, No More MarkingQ263–294
Dr Matthew Glanville, Head of Assessment Principles and Practice, The International Baccalaureate; Joel Kenyon, Science Teacher and Community Cohesion Lead, Dormers Wells High School, Southall, LondonQ295–326
Jamie Njoku-Goodwin, CEO, UK Music; Paul Fleming, General Secretary, EquityQ327–373
Coran Darling, Associate, Intellectual Property and Technology, DLA Piper; Dr Hayleigh Bosher, Senior Lecturer in Intellectual Property Law, Brunel UniversityQ374–411
Lindsey Chiswick, Director of Intelligence, Metropolitan Police; Dr Tony Mansfield, Principal Research Scientist, National Physical LaboratoryQ412–506
Michael Birtwistle, Associate Director, AI and data law & policy, Ada Lovelace Institute; Dr Marion Oswald, Senior Research Associate for Safe and Ethical AI and Associate Professor in Law, The Alan Turing Institute and Northumbria UniversityQ507–538
The following written evidence was received and can be viewed on the inquiry publications page of the Committee’s website.
GAI numbers are generated by the evidence processing system and so may not be complete.
1 ACT | The App Association (GAI0018)
2 ADS (GAI0027)
3 AI & Digital Healthcare Group, Centre for Regulatory Science and Innovation, Birmingham (University Hospitals Birmingham NHS Foundation Trust/University of Birmingham) (GAI0055)
4 AI Centre (GAI0037)
5 AI Governance Limited (GAI0050)
6 Abrusci, Dr Elena (Lecturer, Brunel University London); and Scott, Dr Richard Mackenzie-Gray (Postdoctoral Fellow, University of Oxford) (GAI0038)
7 Academy of Medical Sciences (GAI0072)
8 Ada Lovelace Institute (GAI0086)
9 Alfieri, Joseph (GAI0062)
10 Alliance for Intellectual Property (GAI0118)
11 Assuring Autonomy International Programme (AAIP), University of York; McDermid, Professor John; Calinescu, Professor Radu; MacIntosh, Dr Ana; Habli, Professor Ibrahim; and Hawkins, Dr Richard (GAI0044)
12 BCS - Chartered Institute for Information Technology (GAI0022)
13 BILETA (GAI0082)
14 BT Group (GAI0091)
15 Belfield, Mr Haydn (Academic Project Manager, University of Cambridge, Leverhulme Centre for the Future of Intelligence & Centre for the Study of Existential Risk); igeartaigh, Dr Seán Ó hÉ (Acting Director and Principal Researcher, University of Cambridge, Centre for the Study of Existential Risk & Leverhulme Centre for the Future of Intelligence); Avin, Dr Shahar (Senior Research Associate, University of Cambridge, Centre for the Study of Existential Risk); ndez-Orallo, Prof José Herná (Professor, Universitat Politècnica de València); and Corsi, Giulio (Research Associate, University of Cambridge, Leverhulme Centre for the Future of Intelligence)) (GAI0094)
16 Big Brother Watch (GAI0088)
17 British Standards Institution (BSI) (GAI0028)
18 Burges Salmon LLP (GAI0064)
19 CBI (GAI0115)
20 CENTRIC (GAI0043)
21 Carnegie UK (GAI0041)
22 Center for AI and Digital Policy (GAI0098)
23 Chiswick, Lindsey (Director of Intelligence, Metropolitan Police) (GAI0121)
24 Clement-Jones, Lord (Digital Spokesperson for the Liberal Democrats, House of Lords); and Darling, Coran (GAI0101)
25 Cohen, Michael (DPhil Candidate, University of Oxford); and Osborne, Professor Michael (Professor of Machine Learning, University of Oxford) (GAI0046, GAI0116)
26 Collins, Dr Philippa (Senior Lecturer in Law, University of Bristol); and Atkinson, Dr Joe (Lecturer in Law, University of Sheffield) (GAI0074)
27 Committee on Standards in Public Life (GAI0110)
28 Compliant & Accountable Systems Research Group, Department of Computer Science & Technology, University of Cambridge; and Compliant & Accountable Systems Research Group, Department of Computer Science & Technology, University of Cambridge (GAI0106)
29 Connected by Data (GAI0052)
30 Copyright Alliance (GAI0097)
31 Creative Commons (GAI0015)
32 Crockett, Professor of Computational Intelligence Keeley (Professor of Computational Intelligence, Manchester Metropolitan University) (GAI0020)
33 DeepMind (GAI0100)
34 Department for Digital, Culture, Media and Sport; and Department for Business, Energy and Industrial Strategy (GAI0107)
35 Edwards, Professor Rosalind (Professor of Sociology , University of Southampton); Gillies, Professor Val (Professor of Social Policy and Criminology , University of Westminster); Gorin, Dr. Sarah (Assistant Professor , University of Warwick); and Ducasse, Dr. Hélène Vannier (Senior Research Fellow , University of Southampton) (GAI0035)
36 Employers Lawyers Association (GAI0031)
37 Equity (GAI0065)
38 Fotheringham, Kit (Postgraduate Researcher, University of Bristol) (GAI0042)
39 GSK (GAI0067)
40 Google (GAI0099)
41 Hopgood, Professor Adrian (Professor of Intelligent Systems, University of Portsmouth) (GAI0030)
42 Imperial College London Artificial Intelligence Network (GAI0014)
43 Information Commissioner’s Office (ICO) (GAI0112)
44 Institute for the Future of Work (GAI0063)
45 Institute of Physics and Engineering in Medicine (IPEM) (GAI0051)
46 Leslie, Professor David (Director of Ethics and Responsible Innovation Research, The Alan Turing Institute; and Professor of Ethics, Technology and Society, Queen Mary University of London) (GAI0113)
47 Liberty (GAI0081)
48 Library and Archives Copyright Alliance (GAI0120)
49 Loughborough University (GAI0070)
50 Mason, Mr Shane (Freelance consultant) (GAI0006)
51 Microsoft (GAI0083)
52 Minderoo Centre for Technology and Democracy, University of Cambridge (GAI0032)
53 NCC Group (GAI0040)
54 NICE; HRA; MHRA; and CQC (GAI0076)
55 National Physical Laboratory (GAI0053)
56 Oswald, Dr Marion (GAI0012)
57 Oxford Internet Institute (GAI0058)
58 Oxford Internet Institute, University of Oxford; University of Exeter (GAI0024)
59 Patelli, Dr Alina (Senior Lecturer in Computer Science, Aston University) (GAI0095)
60 Protect Pure Maths (GAI0117)
61 Public Law Project (GAI0069)
62 Publishers Association (GAI0102)
63 Pupils 2 Parliament (GAI0096)
64 Queen Mary University London (GAI0073)
65 RELX (GAI0033)
66 Reed, Professor Chris (Professor of Electronic Commerce Law, Centre for Commercial Law Studies, Queen Mary University of London) (GAI0059)
67 Richie, Dr Cristina (lecturer, TU Delft) (GAI0001, GAI0002)
68 Rolf, Dr Steve (Research Fellow, The Digital Futures at Work (Digit) Centre, University of Sussex Business School) (GAI0104)
69 Rolls-Royce plc (GAI0109)
70 Sage Group (GAI0108)
71 Salesforce (GAI0105)
72 Sanchez-Graells, Professor Albert (Professor of Economic Law, University of Bristol Law School) (GAI0004)
73 School of Informatics, University of Edinburgh (GAI0079)
74 Scott, Mr. Michael (Chair of Trustees, Home-Start Nottingham) (GAI0005)
75 Sense about Science (GAI0078)
76 TUC (GAI0060)
77 Tang, Dr Guan H (Senior Lecturer, Centre for Commercial Law Studies, Queen Mary University of London) (GAI0077)
78 TechWorks (GAI0068)
79 Tessler, Leonardo (PhD in law Candidate, University of Montreal) (GAI0092)
80 The Alliance for Intellectual Property (GAI0103)
81 The Institution of Engineering and Technology (IET) (GAI0021)
82 The LSE Law School, London School of Economics.; and The LSE Law School, London School of Economics. (GAI0036)
83 The Nutrition Society (GAI0007)
84 The Royal Academy of Engineering (GAI0039)
85 The Royal College of Radiologists (RCR) (GAI0087)
86 Thorney Isle Research (GAI0016)
87 Tripathi, Mr Karan (Research Associate , University of Sheffield); and Tzanou , Dr Maria (Senior Lecturer in Law, University of Sheffield) (GAI0047)
88 Trustpilot (GAI0054)
89 Trustworthy Autonomous Systems Hub; The UKRI TAS Node in Governance & Regulation; and The UKRI TAS Node in Functionality (GAI0084)
90 UK BioIndustry Association (GAI0026)
91 UK Dementia Research Institute (GAI0111)
92 UKRI (GAI0114)
93 United Nations Association UK; Article 36; Women’s International League for Peace and Freedom UK; and Drone Wars UK (GAI0090)
94 University of Glasgow (GAI0057)
95 University of Sheffield (GAI0017)
96 University of Surrey (GAI0075)
97 Wayve (GAI0061)
98 Which? (GAI0049)
99 Whittlestone, Dr Jess (Head of AI Policy, Centre for Long-Term Resilience); and Moulange, Richard (PhD student , MRC Biostatistics Unit, University of Cambridge) (GAI0071)
100 Wudel, Alexandra (Political Advisor, German Parliament); Gengler, Eva (PhD Student, FAU Nürnberg); and Center for Feminist Artificial Intelligence (GAI0013)
101 Wysa Limited (GAI0093)
102 medConfidential (GAI0011)
103 techUK (GAI0045)
All publications from the Committee are available on the publications page of the Committee’s website.
|
Number |
Title |
Reference |
|
1st |
Pre-appointment hearing for the Executive Chair of Research England |
HC 636 |
|
2nd |
UK space strategy and UK satellite infrastructure |
HC 100 |
|
3rd |
My Science Inquiry |
HC 618 |
|
4th |
The role of Hydrogen in achieving Net Zero |
HC 99 |
|
5th |
Diversity and Inclusion in STEM |
HC 95 |
|
6th |
Reproducibility and Research Integrity |
HC 101 |
|
7th |
UK space strategy and UK satellite infrastructure: reviewing the licencing regime for launch |
HC 1717 |
|
8th |
Delivering nuclear power |
HC 626 |
|
Number |
Title |
Reference |
|
1st |
Direct-to-consumer genomic testing |
HC 94 |
|
2nd |
Pre-appointment hearing for the Chair of UK Research and Innovation |
HC 358 |
|
3rd |
Coronavirus: lessons learned to date |
HC 92 |
|
Number |
Title |
Reference |
|
1st |
The UK response to covid-19: use of scientific advice |
HC 136 |
|
2nd |
5G market diversification and wider lessons for critical and emerging technologies |
HC 450 |
|
3rd |
A new UK research funding agency |
HC 778 |
2 Alan Turing, “Computing machinery and intelligence”, Mind, vol. 59 (1950), pp 433–460
3 Mr Haydn Belfield, Dr Seán Ó hÉigeartaigh, Dr Shahar Avin, Giulio Corsi (University of Cambridge) and Prof José Hernández-Orallo (Universitat Politècnica de València) (GAI0094)
5 Microsoft and OpenAI extend partnership, Microsoft, 23 January 2023
9 A proposal for the Dartmouth summer research project on artificial intelligence, J. McCarthy, M.L. Minsky, N. Rochester and C. Shannon, 31 August 1955
14 Disinformation Researchers Raise Alarms About A.I. Chatbots, New York Times, 8 February 2023
15 ChatGPT is making up fake Guardian articles. Here’s how we’re responding, The Guardian, 6 April 2023
16 Nvidia’s AI software tricked into leaking data, Financial Times, 9 June 2023
17 Oral evidence taken on 3 May 2023, HC (2022–23) 1324, Q46
19 A pro-innovation approach to AI regulation, GOV.UK, 29 March 2023
20 What’s ahead for Bard: More global, more visual, more integrated, Google, 10 May 2023
21 An important next step on our AI journey, Google, 6 February 2023
22 Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web, Microsoft, 7 February 2023
23 Introducing Microsoft 365 Copilot – your copilot for work, Microsoft, 16 March 2023
25 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 1
27 UK BioIndustry Association (GAI0026)
28 £36 million boost for AI technologies to revolutionise NHS care, GOV.UK, 16 June 2021
29 £21 million to roll out artificial intelligence across the NHS, GOV.UK, 23 June 2023
34 An article in the British Journal of General Practice, published in 2010, described how the inventor of the BMI, Adolphe Quetelet, created the formula: “Adolphe Quetelet’s interest in the emerging discipline of statistics in the mid 1830s saw him collect data on men’s heights and weights at various ages. From this study, which he hoped would allow him to determine the ‘average’ man, he formulated what became known as the Quetelet formula, but which is now known as the BMI”.
40 Exscientia: our mission, accessed 27 June 2023
47 Oral evidence taken before the Liaison Committee on 4 July 2023, HC (2022–23) 1602, Q16 (the Prime Minister)
55 Using AI, scientists find a drug that could combat drug-resistant infections, MIT News, 25 May 2023
57 UK BioIndustry Association (GAI0026)
58 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023
59 Artificial intelligence: in Europe, innovation and safety go hand in hand | Statement by Commissioner Thierry Breton, European Commission, 18 June 2023
60 Sen. Chuck Schumer launches SAFE innovation in the AI age at CSIS, Center for Strategic and International Studies, accessed 27 June 2023
61 A guide to the Online Safety Bill, GOV.UK, 16 December 2022
62 Report of the Committee of Inquiry into Human Fertilisation and Embryology, Wellcome Collection, accessed 17 July 2023
63 Creative Commons (GAI0015), Institution of Engineering and Technology (GAI0021)
71 Connected by Data (GAI0052)
76 Facial recognition technology in law enforcement equitability study: final report, National Physical Laboratory, 5 April 2023
78 Biometric Britain: the expansion of facial recognition software, Big Brother Watch, 23 May 2023, p. 67
79 Big Brother Watch (GAI0088)
84 WARNING: Beware frightening new ‘deepfake’ Martin Lewis video scam promoting a fake ‘Elon Musk investment’ – it’s not real, Money Saving Expert, 7 July 2023
86 Hello, this is your bank speaking: HSBC unveils voice recognition, Financial Times, 19 February 2016
87 You may have heard about AI defeating voice authentication. This research kinda proves it, The Register, 28 June 2023
88 ADS (GAI0027), Royal College of Radiologists (GAI0087)
90 The Ada Lovelace Institute (GAI0086)
91 CMA launches initial review of artificial intelligence models, GOV.UK, 4 May 2023
92 “We must regulate AI,” FTC Chair Khan says, Ars Technica, 3 May 2023
93 The Ada Lovelace Institute (GAI0086)
95 Defined in the independent Future of Compute Review commissioned by the UK Government and published in March 2023 as “… the systems assembled at scale to tackle computational tasks beyond the capabilities of everyday computers. This includes both physical supercomputers and the use of cloud provision to tackle high computational loads”.
98 Plan to forge a better Britain through science and technology unveiled, GOV.UK, 6 March 2023
99 PM London Tech Week speech, GOV.UK, 12 June 2023
101 Burges Salmon LLP (GAI0064)
105 Public Law Project (GAI0069)
106 AI and Digital Healthcare Group, Centre for Regulatory Science and Innovation, Birmingham (University Hospitals Birmingham NHS Foundation Trust/University of Birmingham) (GAI0055)
107 A New National Purpose: AI Promises a World-Leading Future of Britain, The Tony Blair Institute for Global Change, 13 June 2023
108 Creative Commons (GAI0015)
110 Generative AI Systems Aren’t Just Open or Closed Source, Wired, 24 May 2023
111 GPT-4 Technical Report, OpenAI, 14 March 2023
112 OpenAI co-founder on company’s past approach to openly sharing research: ‘We were wrong’, The Verge, 15 March 2023
114 Streaming services urged to clamp down on AI-generated music, Financial Times, 12 April 2023
121 The government’s code of practice on copyright and AI, GOV.UK, 29 June 2023
122 HM Government response to Professor Dame Angela McLean’s Pro-Innovation Regulation of Technologies Review: Creative Industries, GOV.UK, 14 June 2023, p. 7
123 The government’s code of practice on copyright and AI, GOV.UK, 29 June 2023
125 Artificial Intelligence and Intellectual Property: copyright and patents: Government response to consultation, GOV.UK, 28 June 2022
126 The Library and Archives Copyright Alliance (GAI0120)
127 Dr Jennifer Cobbe and Dr Jatinder Singh, Compliant & Accountable Systems research group, Department of Computer Science & Technology, University of Cambridge (GAI0106)
129 The Ada Lovelace Institute (GAI0086)
130 Announcing Google DeepMind, Google DeepMind, 20 April 2023
132 Oral evidence taken on 3 May 2023, HC (2022–23) 1324, Q47
136 Oral evidence taken on 3 May 2023, HC (2022–23) 1324, Q51
137 Oral evidence taken before the Liaison Committee on 4 July 2023, HC (2022–23) 1602, Q17 (the Prime Minister)
138 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023
139 MEPs ready to negotiate first-ever rules for safe and transparent AI, European Parliament, 14 June 2023
140 Readout of White House meeting with CEOs on advancing responsible artificial intelligence innovation, the White House, 4 May 2023
141 Sen. Chuck Schumer launches SAFE innovation in the AI age at CSIS, Center for Strategic and International Studies, accessed 27 June 2023
143 UK to host first global summit on artificial intelligence, GOV.UK, 7 June 2023
144 British and Irish Law Education Technology Association (GAI0082)
145 Tech entrepreneur Ian Hogarth to lead UK’s AI Foundation Model Taskforce, GOV.UK, 18 June 2023
146 We must slow down the race to God-like AI, Financial Times, 13 April 2023
147 AI systems ‘could kill many humans’ within two years, The Times, 5 June 2023
148 Dr Jess Whittlestone and Richard Moulange (GAI0071)
149 Oral evidence taken before the House of Lords Artificial Intelligence in Weapons Systems Committee on 8 June 2023, Q104 (Lord Sedwill)
150 Oral evidence taken before the House of Lords Artificial Intelligence in Weapons Systems Committee on 8 June 2023, Q107 (Lord Sedwill)
151 Statement on AI Risk, Center for AI Safety, 30 May 2023
154 AI is supposedly the new nuclear weapons — but how similar are they, really?, Vox, 29 June 2023
155 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 50
156 The Algorithm: how existential risk became the biggest meme in AI, MIT Technology Review, 19 June 2023
157 The Algorithm: seven things to pay attention to when talking about AI, MIT Technology Review, 29 May 2023
158 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023
159 AI regulation: a pro-innovation approach—policy proposals, GOV.UK, accessed 27 June 2023
160 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 5
161 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 6
162 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, pp. 6–7
163 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, pp. 72–73
164 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 6
170 Oral evidence taken before the Liaison Committee on 4 July 2023, HC (2022–23) 1602, Qq. 19–20 (the Prime Minister)
171 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 14
172 A pro-innovation approach to AI regulation, CP 815, Department for Science, Innovation and Technology, 29 March 2023, p. 15
173 Digital Regulation Cooperation Forum, GOV.UK, accessed 19 June 2023
174 ADS (GAI0027), RELX (GAI0033), NCC Group (GAI0040)
179 Tech entrepreneur Ian Hogarth to lead UK’s AI Foundation Model Taskforce, GOV.UK, 18 June 2023
180 Tech entrepreneur Ian Hogarth to lead UK’s AI Foundation Model Taskforce, GOV.UK, 18 June 2023
181 Initial £100 million for expert taskforce to help UK build and adopt next generation of safe AI, GOV.UK, 24 April 2023
182 Proposal for a Regulation laying down harmonised rules on artificial intelligence, European Commission, 21 April 2021
183 MEPs ready to negotiate first-ever rules for safe and transparent AI, European Parliament, 14 June 2023
184 MEPs ready to negotiate first-ever rules for safe and transparent AI, European Parliament, 14 June 2023
185 EU AI Act: first regulation on artificial intelligence, European Parliament, accessed 19 June 2023
186 Europe takes another big step toward agreeing an AI rulebook, TechCrunch, 14 June 2023
189 European VCs and tech firms sign open letter warning against over-regulation of AI in draft EU laws, TechCrunch, 30 June 2023
190 Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, White House Office of Science and Technology Policy, 20 October 2022
191 Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, White House Office of Science and Technology Policy, 20 October 2022, p. 4
192 Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, White House Office of Science and Technology Policy, 20 October 2022, pp. 5–7
193 Readout of White House meeting with CEOs on advancing responsible artificial intelligence innovation, the White House, 4 May 2023
194 Sen. Chuck Schumer launches SAFE innovation in the AI age at CSIS, Center for Strategic and International Studies, accessed 27 June 2023
195 Written Testimony of Sam Altman, Chief Executive Officer OpenAI Before the U.S. Senate Committee on the Judiciary Subcommittee on Privacy, Technology, & the Law, 16 May 2023
198 UK to host first global summit on artificial intelligence, GOV.UK, 7 June 2023
199 List of member states, International Atomic Energy Agency, accessed 7 July 2023
200 Oral evidence taken before the Liaison Committee on 4 July 2023, HC (2022–23) 1602, Q18 (the Prime Minister)
202 Mr Haydn Belfield, Dr Seán Ó hÉigeartaigh, Dr Shahar Avin, Giulio Corsi (University of Cambridge) and Prof José Hernández-Orallo (Universitat Politècnica de València) (GAI0094)