AI ROADMAPUK AI COUNCIL

The AI Council is an independent expert committee. It provides advice to theUK Government, as well as high-level leadership of the Artificial Intelligence (AI) ecosystem.

This independent report draws on the expertise of its members and those in its widerecosystem to summarise four pillars on which to build the UK's future in AI. It invites action

across government to keep the UK at the forefront of safe and responsible AI.

Version 1(JA-B_DE)

Executive summary 2

16 recommendations to help the government develop a UK National AI Strategy 4

Introduction 6

1. Research, Development and Innovation 10

The Alan Turing Institute: local presences and global reach 11

Challenge-led innovation 12

AI to transform research, development and innovation 14

2. Skills and Diversity 16

What everyone needs to know 16

Further, Higher and Graduate Education 17

Supporting every teacher 18

Lifelong learning and an online academy for AI 18

3. Data, Infrastructure and Public Trust 20

Trustworthy and accessible data 20

Public trust and good governance 22

Digital Twins and Living Labs 24

A globally leading role 25

4. National, Cross-sector Adoption 28

Business as smart adopters 28

Supporting high growth AI startups 29

Enabling public sector adoption 30

Health and social care 31

Climate change 32

Defence and security 33

Acknowledgements 36

UK AI COUNCIL | AI ROADMAP

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As Artificial Intelligence (AI) becomesembedded in people’s lives, the UKfinds itself at a pivotal moment.

Estimates show that AI could deliver a10% increase in UK GDP in 2030.1 Ifapproached correctly, this would offerhuge benefits to the economy, torecovery and resilience, the environmentand for people from all walks of lifeacross all parts of the UK. This summaryreport by the AI Council sets out long-term ambitions and suggests near-termdirections for all governmentdepartments, with the aim of cementingthe UK as one of the very best places inthe world to live with, work with anddevelop AI.

The UK starts from a place of strength inresearch, enterprise and regulation, andwith its history of recent support for AI itstands among the best in the world. TheUK government’s ambition should push

for scale and reliability in areas of uniqueadvantage. To be influential in attractingtalent, shaping global markets and globalgovernance we call on the government tobuild a UK National AI strategy that scalesup its investment for a decade andbeyond.

This report has two underlying messages.The first is that we need to “double down”on recent investment the UK has made inAI. The second message is that we mustlook to the horizon and be adaptable todisruption. Support for AI needs to reflectthe rapid pace and evolution of thescience & technology and its applications.This means staying at the forefront of thedevelopment of AI and integratingapproaches to ethics, security and socialimpacts and planning for the next 10-50years. The UK will only feel the fullbenefits of AI if all parts of society havefull confidence in the science and thetechnologies, and in the governance and

Executivesummary

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regulation that enable them. Thatconfidence will depend on the existenceof systems that ensure full accountability,clear ethics and transparency. Developingthe best science and the most robustapplications requires commitment to anambitious programme of investment intalent; one that promotes cutting edgeskills and does so in ways that makes AIaccessible in ways that are more diverseand inclusive.

Given the breadth and significance ofpotential AI applications, this Roadmap isconcerned with overcoming thechallenges of enabling change. It sets outsuggested directions across four pillars:Research, Development & Innovation;Skills and Diversity, and Data,Infrastructure and Public Trust - andthen, in the final section, addresses somespecific measures to support adoptionand the key areas of health, climate anddefence.

This Roadmap and its recommendationsreflects the views of the Council as well as100+ additional experts. We recognise itwill not be possible to achieve all at once,and this is why a National AI Strategy isneeded to prioritise and set a time framethat will position the UK for success. Inorder to support the government, we areready to convene workshops with thewider ecosystem in order to capturemore detail and work together to ensurethat a future National AI Strategy enablesthe whole of the UK to flourish.

1The economic impact of artificial intelligence on the UK economy, PwC (June 2017)

“

“A National AI Strategy is needed to

prioritise and set a time frame that willposition the UK for success

The UK will only feel the full benefits of AI if allparts of society have full confidence in the scienceand the technologies, and in the governance and

regulation that enable them

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Research, Developmentand Innovation

1. Scale up and make sustainable publicsector investment in AI; ensureconsistent access to top talent fromaround the world; and find new waysto bring researchers, disciplines andsectors together. Build on thecommitments in the government’s R&DRoadmap2 and suggestions in the soonto be published UKRI AI review3.

2. Cement The Alan Turing Institute as atruly national institute, with a set ofregional investments that draw onstrengths from across the UK.Provide assured long term publicsector funding that will give the Turingand others the confidence to plan andinvest in strategic leadership for the UKin AI research, development andinnovation.

3. Ensure moonshots, as described inthe R&D Roadmap as challenge-led,high-risk, scalable programmes, areboth advancing and leveraging AI.These could tackle fundamentalchallenges such as creating“explainable AI”, or important goals inany area where AI can contributestrongly, such as the UK Digital Twinprogram or developing smart materialsfor energy storage in the move towardsNet Zero carbon emissions.

Skills and Diversity

4. Scale up and commit to an ongoing 10year programme of high level AI skill-building. This would include researchfellowships, AI-relevant PhDs acrossdisciplines, industry-led Masters and level7 apprenticeships.

5. Make diversity and inclusion a priority.We suggest benchmarking andforensically tracking levels of diversity tomake data-led decisions about where toinvest and ensure that underrepresentedgroups are given equal opportunity andincluded in all programs.

6. Commit to achieving AI and data literacyfor everyone. The public needs tounderstand the risks and rewards of AI sothey can be confident and informedusers. An online academy forunderstanding AI, with trusted materialsand initiatives would support teachers,school students and lifelong learning.

16 recommendations to help thegovernment develop a UK National AI Strategy

AI ROADMAP 5

Data, Infrastructureand Public Trust

National, Cross-sectorAdoption

7. Consolidate and accelerate theinfrastructure needed to increaseaccess to data for AI. Invest in therelevant organisations, link generalprinciples to specific applications, andpursue initiatives for pump priminginnovation and enabling safe datasharing for valuable uses.

8. Lead the development of datagovernance options and its uses.The UK should lead in developingappropriate standards to frame thefuture governance of data.

9. Ensure public trust through publicscrutiny. The UK must lead in findingways to enable public scrutiny of, andinput to, automated decision-makingand help ensure that the public cantrust AI.

10.Thoughtfully position the UK withrespect to other major AI nations.Building on its strengths, the UK has acrucial opportunity to become a globallead in good governance, standardsand frameworks for AI and enhancebilateral cooperation with key actors.

11.Increase buyer confidence and AIcapability across all sectors and allsizes of company. Support investmentfor local initiatives to enable safe value-creating innovation and improve thedata maturity needed for AIinnovation.

12.Support the UK’s AI startup vendorcommunity. Enable greater access todata, infrastructure, skills, compute,specialist knowledge and funds.

13.Enable robust public sectorinvestments in AI, building capabilityin the use of data, analytics and AI toensure intelligent procurement of AI aspart of projects for public benefit.

14.Use AI to meet the challenges of NetZero carbon emissions. Work onaccess to data, governance, to developcleaner systems, products andservices.

15.Use AI to help keep the country safeand secure. Work with governmentdepartments/agencies and defenceand security companies to ensure AI isavailable to assess and respond tomodern defence and security threatsand opportunities.

16.Build on the work of NHSX and othersto lead the way in using AI to improveoutcomes and create value inhealthcare. The UK’s comparativeadvantage will depend on smartstrategies for data sharing, newpartnership models with SMEs andskill-building.

2UK Research and Development Roadmap, UK Government (July 2020)3 [To be published early 2021]

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Artificial Intelligence (AI) is breakinginto the mainstream of societies andthe global economy.

The history of AI is characterised bycycles of boom and bust, with waves ofexcessive hype followed by “AI winters”.This time things are different: advances inmachine learning and neural networkshave led to great advances in fields suchas computer vision and speechrecognition, and natural languageprocessing, with numerous applicationsused by hundreds of millions of peopleon a daily basis. Safely harnessed AI isrevolutionising certain types of tasks,disrupting business models, andcomplementing and enhancing humancapabilities in wholly new ways. Thesetremendous advances have come fromincreasing the sizes of training data sets,and the associated networks andcompute power, by factors of a million ormore, along with modifications to the

underlying algorithms, many of whichhave been with us in some form for along time. This accounts for tens ofbillions of dollars in global annual valuecreation, and looks set to increasemarkedly over the coming decade.4

The UK has fundamental strengths in AIwhich reach back to the intellectualorigins of the science, with figures likeAlan Turing, Joan Clarke, Donald Michieand many others, to its ability to debateand enact sound governance of newtechnologies. Successive governments,businesses and civil groups have workedhard to make increasing amounts of dataand information more available in waysthat combine safety and usefulness.

In recent years, the UK has been the siteof an array of initiatives aimed atdeveloping and deploying AI across theeconomy for society. Building on thesewith focus and structure will ensure AI is

Introduction

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an essential part of tackling the majorchallenges of economic recoveryfollowing the COVID-19 pandemic, oflevelling up the UK economy through thecreation of jobs and prosperity beyondLondon and the South East, of ensuringnew forms of health and social care, ofreaching net zero carbon emissions, andof ensuring resilience to future economic,health, or environmental shocks. It willalso create entirely new opportunities forhuman flourishing.

These fundamental strengths and recentinitiatives have helped the UK to beamongst the global leaders in many areasof AI, from accelerating drug discovery5 tohelping businesses make decisions thataccurately factor in climate volatility.6

However, the next few months and yearswill be crucial in determining where theUK places its desired level of ambition inAI, as massive investments bygovernments and businesses around theworld are already priming themselves toinfluence the global conversation. Thescale of effort elsewhere must not beunderestimated: Germany hascommitted €3.1 billion towards a nationalAI strategy up to 20257, and France haspledged €1.5 billion up to 2022, withalmost half earmarked for research.8While the UK has played a leading role inthe development of AI, the US hasrecently announced $1 billion over the

coming five years to open 12 newinstitutes that will keep the country at theforefront of research in AI and quantumcomputing9, and like-minded countriessuch as Canada and Germany are leadingon commercialisation as measured bypatents.10

The Council calls for a UK National AIStrategy that creates an even strongergeneral base of support for AI. Scotlandhas already embarked on an AI Strategyand the UK strategy should be created inconsultation with the devolvedadministrations and the widerecosystem.

The UK cannot, and need not try to,compete equally in all economic sectorsso it must shape its strategy carefully toensure it prioritises and is able to actselectively to lead and to capture marketsaround the world. In doing this it willneed to consider its options forcollaboration and competition within theinternational scientific and economiclandscapes.

4 Some estimates show that leading nations could gain an extra 20-25% in economic growth and productivity gains through AI over the next decade.Modelling the impact of AI on the global economy, McKinsey (September 2018)5Biotech harness AI in battle against Covid-19, Financial Times (May 2020)6 UK tech for a sustainable future, Tech Nation (2020)7 Germany AI Strategy Report, European Commission (March 2020)8 France AI Strategy Report, European Commission (February 2020)9 The Trump Administration Is Investing $1 Billion in Research Institutes to Advance Industries of the Future, The White House (August 2020)10 WIPO Technology Trends 2019: Artificial Intelligence, World Intellectual Property Organisation (January 2019)11 Developing an AI Strategy for Scotland, Scotland’s AI Strategy (2019)

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The Council has considered how todiffuse the development of AI acrosssections of society that would help toboth improve the lives of its citizens andbring economic benefits in the form ofrecovery and resilience. This has resultedin four pillars:

• support for research, developmentand innovation;

• literacy in AI across the populationwith enhanced AI-related skills at alllevels of educational and lifelongattainment;

• sound physical, digital and virtualinfrastructure, including robust andflexible regulation as a form of socialinfrastructure, resulting in goodgovernance that boosts public trust;

• and pragmatic approaches tonational, cross-sector adoption thatmeaningfully advances public andprivate sector applications.

This Roadmap sets out the requirementsin each of these areas in more detail andbuilds on the 2017 Hall-Pesenti AI Review.Its premise is that, in most areas, whatthe UK needs to do is to “double down”on the best of the investments it hasalready made, and to accelerate what’sproved to be ‘working building’ capabilityacross organisations and sectors. Doingthis is not easy, and least of all in the faceof jobs at risk that could be made morevulnerable by the twin pressures of theCOVID-19 pandemic and automation.12

Fundamentally, it requires commitmentsof a size, and for sustained timescales,that are often only possible at times ofdisruption such as the one the world isgoing through now with its response andrecovery from the COVID-19 pandemic. Ittherefore requires a degree of collectivevision and of boldness, combining thisleadership with the confidence to createspaces for entrepreneurism in all parts ofsociety and the economy in order to findnew ways to use AI to benefit everyone.

In a small number of important areas thisRoadmap points in new directions. As AIbecomes more integrated into daily life,developing trustworthy technology is vitaland businesses will require a novelapproach to good governance andconsideration of the ethical and societalimplications of AI as part of the designprocess, with greater multi-disciplinaryand cross-disciplinary approachesthroughout. The UK needs to develop aworld-class enabling environment withstrong but flexible legislation and well-resourced, capable regulators able toprovide responsive guidance and timelyoversight.

This Roadmap is not a draft strategy, andnor is it an instruction manual. This is aset of recommendations and werecognise it will not be possible toachieve all at once. This is why a NationalAI Strategy is needed to prioritise and seta time frame. We are ready to conveneworkshops across all of the areas laid outin this Roadmap, and more, to capturethe full spectrum of thought across the

12 Who is at risk?, The Royal Society for Arts, Manufactures and Commerce (October 2020) finds that, broadly speaking, industries with the highest level of furlough take-up are morelikely to be at risk of automation.

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UK in a way that would lend itself to thedevelopment of a UK National AI Strategy.We have set out a clear direction that webelieve will accelerate the UK to a positivetechnological future and start thenecessary conversations needed tocreate a strategy that will enable thewhole of the UK to flourish.

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AI’s full potential will only be realisedin the UK if the government continuesto create the conditions for leadingedge research, development andinnovation in AI for the public andprivate sectors, and if it creates theconditions for AI to work in and withmultiple disciplines and areas ofsociety and the economy.

In common with some other parts of thisRoadmap, what can seem like arestatement of general principles thatcould be taken for granted is, in fact, achallenging requirement for sustainedsystemic change.

The government’s R&D Roadmap13 makesan excellent start in outlining some of theareas of systemic change that willparticularly benefit AI. One of the mostimportant commitments is to make theUK a place that is open and inviting totop talent. Demand for AI talent is at an

all-time high, and there is more to do toattract and retain the very best to theUK’s shores. Skills training, both at aresearch and workforce level, is vital totranslate research, development andinnovation, and suggestions are detailedin the forthcoming section.

The government R&D Roadmap includesvaluable commitments to strengtheningthe interactions between discovery andapplied research, innovation,commercialisation and deployment. Thisis vital: funding either excellent basic orapplied research in isolation risks theoutputs and the people being easilytransferred to other countries, so anycompetitive advantage can be easily andquickly lost, especially to other countriesthat have more resources than the UK.The R&D Roadmap also signifies supportfor entrepreneurs; creating a more place-based approach to research,development and innovation; and the

1. Research,Developmentand Innovation

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aspiration to be more imaginative in waysof ensuring that research and innovationis directly responsive to the needs andaspirations of society. This last pointechoes strongly the views of the AICouncil’s ecosystem.

In addition to building on these broaddirections, there are three areas whichare more specific to AI and on which thegovernment needs to take significantaction.

The Alan Turing Institute: localpresences and global reach

The first is to build on the existing successof The Alan Turing Institute as a nationalchampion for AI research, developmentand innovation in the UK. In a relativelyshort period of time the Turing hasdeveloped to play a pivotal role inpromoting AI research across sectors andin attracting talent by leveraging itsgrowing network. Over the past year, theTuring has attracted 77 new TuringFellows, bringing the total to 369, and 21new research associates alongside 116events across their research portfolio, anda student enrichment scheme intakerepresenting 36% female and 32% fromBAME backgrounds.14 It has also reachedout to crowdsource best practice for datascience and AI research, was pivotal increating a new discipline in data-centricengineering, and has worked acrossgovernment to provide policy advice andoperational support.

Concentrating on these beginnings, anenhanced and reshaped Turing will be acrucial part of the necessary research,development and innovationinfrastructure but, to play that role fully,some changes are needed. The UK’sresearch system as a whole cannot reachits full potential while the originalmembership model has the effect ofinadvertently excluding excellent researchteams across the four nations. The UKneeds the Turing to have a full nationalreach so that it can play its part inlevelling up and in supporting talent andgreat ideas wherever they start. To dothis, it will need to be able to bringtogether researchers, businesses, civilgroups and the public sector to makegreat ideas work at all geographic levelsand at a sector-wide scale.

A new Turing model might, for example,extend both its virtual and physicalpresences to energise more regional andlocal activity. There are several ways toachieve such aims, building on theTuring’s convening role in the UKlandscape to join up and build on existingteams and infrastructure. This could bedelivered through Turing acting as aheadquarters and convenor of anationally network of centres-of-excellence or hubs, which may be physicalor virtual, which would not only advanceAI research but also development,innovation and deployment into theregions. Across these centres, skillstraining will be at both the research leveland, most importantly, in apprenticeships,upskilling and reskilling regional

13UK Research and Development Roadmap, UK Government (July 2020)14Annual Report 2019-20, The Alan Turing Institute (2020)

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workforces to exploit the opportunities ofAI across sectors of the UK economy.Models for such centres might be basedon, for example, UK’s National CyberSecurity Centre, or focus on regions tosupport AI research, development andinnovation capability to support the localeconomy.

Well-funded institutes are becominghighly valued components of national AIstrategies, and as a national institute ofglobal standing, the Turing also needs theassured long term public sector fundingthat will give it and others the confidenceto plan and invest for the medium termand strategic leadership for the UK, withthe needs of academia, businesses, civilgroups and the public sector reflected inits strategic direction. A well-funded UKinstitute would not only signal the UK’scommitment to remaining a globallyleading player over the coming decades,it could also promote the UK’s intereststhrough collaborations with internationalpartners and enable it to play asignificant role in building theinternational infrastructure discussed aspart of the third pillar in this Roadmap.

Sustainable funding would enable theTuring to provide visible nationalleadership on matters of AI security andethics, and use this leadership role andconnectivity across the landscape toensure the UK remains at the forefront ofnext-generation AI developments, suchas those involving machine learning fromsmall data sets, and machine learning aspart of wholly new types of neural

interface technologies.15 This addedfocus on how to leap-frog currentmachine learning-based AI technologieswould enable the UK, through the Turingand its network-trusted and neutralstatus, to also become the place forExplainable AI, bringing us closer to safe,responsible and trustworthy AI.

Challenge-led innovation

The significant promise of AI and its highrisk, high reward, aspirational naturelends itself to goal-directed “moonshot”programmes in the style often associatedwith ARPA-esque models.16a Properlydesigned and delivered moonshotprogrammes play to AI’s strengths byrequiring people to work acrossboundaries and existing organisationalstructures, and to build newrelationships, networks and commonlanguages in order to develop entirelynew solutions to big challenges. Inaddition to this, there is a need for theUK to consider further the question ofhow AI interacts with people, and what itmeans to create effective human-AIpartnerships.

One such moonshot, directly relevant toAI, might involve a major challenge aimedat developing and establishingappropriate methods for safe, ethical,explainable and reproducible AI whichwill accelerate its use across manysectors. Another would be thedevelopment of an innovation ecosystemenabled by the UK Digital Twinprogramme, as discussed later in this

RESEARCH, DEVELOPMENT AND INNOVATION 13

Roadmap, within which AI would usethese virtual worlds to explore real worldoptimisations that are beyond humanscale.

AI should also be built into moonshots inother sectors where it could play a majorpart, for example to radically acceleratematerials science to develop newmaterials that can better store energyfrom renewable sources, or in solving forreal-time control of the increasinglycomplex electricity distribution network asthe country moves towards net zeroemissions.

Across all the potential types of challenge-based funding, programmes need to beselected and designed to draw in andaccelerate AI’s integration acrossdisciplines and sectors. One suggestionwould be to select broad sectors that aremore mature, and thus ripe for AIadoption than others, and use themission-based approach to focus minds.This would bring together basic andapplied research and multiple disciplinesacross research horizons andopportunities for systems development,helping to mature AI as a discipline.

The specific vehicle for deliveringmoonshots is a ripe topic for discussion,but a forward-looking framework forresearch, development and innovationconsisting of a series of labs, whichconceive of and build fully engineeredprototypes and platforms, could offer aflexible model of experimentation. Theselabs could generate vision-based

sequences of projects around moonshotchallenges and could serve those with aninterest in working between industry,academia and the public sector.

By providing significant signals aboutpotential future developments,moonshots create space for informedpublic and business engagement with theemerging science and applications. Underthese circumstances the science andtechnology is not simply communicatedto; it is inspired by, and carried out with,individuals, communities and businesses.Such space enables all those involved toexplore how AI interacts with people andwhat it means to create human-AIpartnerships. In turn, the projects willhelp define responsible and ethicalpractices to meet society’s needs andaspirations for a technology that behavesas people and businesses hope andexpect: safely, securely and reliably.

15 iHuman perspective: Neural interfaces, The Royal Society (September 2019)16aThe US’ Advanced Research Project Agency (ARPA) is a model designed to concentrate funding around pivotal investments in breakthrough technologies, often for nationalsecurity purposes. DARPA (for Defense) is the most well-known ARPA agency in the US and is credited with developing a number of disruptive technologies such as stealthtechnology, the foundations of the Internet, and miniature GPS receivers.

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AI to transform research,development and innovation

Capitalising on the promise of AI willcontinue to require investment in basicresearch into the fundamentals of AIitself. Researchers, increasingly workingacross disciplines, will need to addresssome of the fundamental challenges thatremain, such as establishing a commonlanguage through which to develop datascience projects or interoperabilitybetween datasets, which can drive smartcollaboration. AI systems often rely on anarrow disciplinary base, and this needsto be broadened to cover an array ofdomains in an intelligent way. AItechnologies need to get better atextending into areas where they do nothave structured data, and to navigateeffectively through that.

At the same time AI is transforming thenature of research itself, with examples inparticle physics and bioinformatics.16b Forthe UK to retain its status as a globalleader in research, it needs to stay wellabreast of the potentially disruptiveeffects of AI and other forms of datascience which are beginning to beincorporated into all forms of knowledgecreation, including the social sciencesand the humanities. Given the availabilityof massive data sets and computerpower, such disruptions could rangefrom game-changing increases inefficiency in routine tasks such assearching texts or large observationaldata sets, to entirely new forms ofenquiry brought about by AI’s ability to

spot new patterns and prompt newquestions and insights, often acrossexisting disciplinary boundaries.

16b AlphaFold: a solution to a 50-year-old grand challenge in biology, DeepMind (November 2020)

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16 AI ROADMAP

The Council’s vision is for everyone tobe able to live confidently with AI, andfor those who go on to work with itand to build it to do so with the verybest foundation.

For the most technically talentedindividuals we need to guarantee a best-in-class postgraduate offering to accessglobally attractive pathways that willcontribute to the country’s renownedresearch base.

Success will depend on increasing thediversity of people working on and with AIuntil those AI communities reflect thesociety they inhabit. It’s important tofocus on diversity; not as an afterthought,but because doing so will generatecrucial research questions and lead tothe widest and most innovative range offuture technologies which will serve theneeds of the people. There are wonderfulexamples of emergent leadership,

creativity, and energy in promotingdiversity but it is not enough. We suggestbenchmarking and forensically trackinglevels of diversity and inclusion in AI asthe NCSC17 has recently done for cybersecurity, to make data-led decisionsabout where to invest and ensure thatunderrepresented groups are included infunding programs. Government must domore to encourage the best of theexisting initiatives where diversity isenabled, and to expand with their ownschemes and campaigns, if the needle isto be sufficiently moved.

What everyone needs to know

The UK needs to set itself challenging butrealistic goals to ensure that every childleaves school with a basic sense of howAI works. This is not just aboutunderstanding the basics of coding orquantitative reasoning, or to describe themathematical concepts; nor is it simply

2. Skills andDiversity

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about ethics. It is about knowing enoughto be a conscious and confident user ofAI-related products; to know whatquestions to ask, what risks to look outfor, what ethical and societal implicationsmight arise, and what kinds ofopportunities AI might provide. Without abasic literacy in AI specifically, the UK willmiss out on opportunities created by AIapplications, and will be vulnerable topoor consumer and public decision-making, and the dangers of over-persuasive hype or misplaced fear.

Over time, AI needs to be built into thecurriculum as a specialist subject. As wellas being its own subject AI needs to bepart of computer science, citizenshipstudies, and as part of new ways of doingother subjects such as geography orhistory. These changes may take a decadeto complete but getting there is essential.For now, the government should focusenergy on the most effective existingcurriculum enrichment initiatives, not asalternatives to fundamental changes tothe curriculum, but because they provideopportunities to accelerate change in theshort term.

Further, Higher and GraduateEducation

Since the AI Review in 2017, thegovernment has invested significantly inschemes to promote graduate-levellearning through Apprenticeships,Masters courses and PhD training. This isa good start, but only that. Currentnumbers would suggest there is unmet

demand to justify a five to tenfoldincrease in places compared to thenumbers in the AI Review.

To ensure a steady supply of skilledentrants to the workforce, and to sendthe right signals to young people makingfurther academic choices, the UK shouldcommit to scaling up its programmessignificantly at all levels of education andmaintain this commitment for at least adecade.

Such a scale up would include AI-relatedLevel 7 apprenticeships, Mastersprogrammes, Centres for DoctoralTraining and novel forms of PhDs. It wouldextend the scope of the schemes toinclude AI-related courses in disciplinesoutside those traditionally associated withdata sciences, with a review of digitalapprenticeship standards to identifywhere additional AI knowledge and skillsought to be unlocked. It must also includethe postgraduate AI and data scienceconversion course programmeannounced by the government last year,whose 2,500 places for people to developnew skills or re-train. Properlyimplemented, this commitment wouldhelp level up access to economicdevelopment across regions. We mustalso ensure that no graduate studentleaves university without some groundingin AI and its applications.

17Decrypting diversity: Diversity and inclusion in cyber security, NCSC (July 2020)

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Meanwhile the Turing AI Fellowshipsprogramme for established researchers,an international fellowship programmefor AI in the UK in a partnership betweenUKRI, the Office for AI, and the Turing, hasalready seen a successful first wave,18

alongside the first batch of Turing AIAcceleration Fellowships.19 In line with the2017 AI Review, this programme mustcontinue to be developed and expandedso that momentum can be built to attractworld-leading talent to the UK.

These efforts, directed at the supply side,would allow graduates and apprenticesto advance into emerging AI professions.They would need to be delivered acrossthe country and to be available to youngpeople of all backgrounds, ensuring thatthe UK’s future AI workforce reflects thediversity of background and thoughtneeded to develop world leading AIcapabilities that tackle the issues whichmatter most. Success will depend oncollaboration between universities,colleges and businesses, not only interms of how academia can help supportcontinuous learning and upskilling withinbusinesses, but also through businesssupport such as mentoring, real life usecases and data to academia.

Supporting every teacher

The key to progress in schools is toenable teachers to have confidence inthe basics themselves, so they can makethe best possible choices about how toengage their students. One way to makeprogress relatively fast would be to

extend the work of the NCCE, supportingit to provide comprehensive curriculumresources that cover AI at all Key Stages,and providing an incentive andopportunity for teachers to learn anddevelop their AI knowledge. Acomprehensive programme aimed at allteachers and with a clear deadline forcompletion would enable every teacherconfidently to get to grips with AIconcepts in ways that are relevant totheir own teaching.

As in many other subjects, learningshould combine access to knowledgeabout AI with exercises based on tacklingreal-world problems in ways thatencourage the skills associated withproblem-solving (including creativity andresilience) and team-working. Thereforenew curriculum resources need toinclude curated data sets that will enableteachers to create the case studies andexercises that will in turn create thefamiliarity that leads all young people toincreased confidence and data literacy.

Lifelong learning and an onlineacademy for AI

Looking across society, the scale of re-skilling and up-skilling that will berequired is vast when you consider apopulation whose working lives may lastmore than half a century beyond the timethey left school, college or university. TheUK must contend with changing businessmodels in goods and services, and thechanging nature of professions andtrades, and the priority of levelling up.

SKILLS AND DIVERSITY 19

These challenges point to the need forsignificant change in the contractbetween the state, individual andemployer and a step change in the scaleof continuous professional developmentand the ability to move between types ofwork.

While these socio-economic trends cutacross sectors and subjects, theyrepresent an even more urgent challengein AI where the required skills haverecently emerged and are still changingrapidly.20 The government shouldconsider the two following major steps.

The first is extending business tax relieffrom investment in research,development and innovation toinvestment in specified forms of retrainingand upskilling, including AI.

A second step would be to enable thecreation of an Online Academy for AI.Such an Academy could provide a key partof the social infrastructure necessary tosupport systemic change. Properlydesigned, such an Academy wouldprovide the opportunity efficiently andcreatively to meet several overlappingneeds which emerge from different partsof this Roadmap. It might start byproviding entry-level material to supportlife-long learning. Ideally, this would beaccredited in a recognisable and portableway. In doing this it could also provide anentry-point and repository for onlinelearning aimed at teachers,complementing the work of bodies suchas the NCCE, and it would encourage the

growing national community of productmanagers retooling for the future in AI.Either separately, or integrated into theAcademy, a ‘data skills observatory’ toidentify and address the on-going andevolving data skills deficit function thatwould otherwise hinder AI innovationwould also be needed.

While the Academy’s main objectivewould be to enable more people to liveand work with AI, it would also be wellplaced to help connect and support localinitiatives involving public engagementand public dialogue as part of responsibleinnovation in new AI deployments,including moonshots. This sustained anddistributed resource, rather than, say, asingle educational or promotionalcampaign, would be an integral part ofnegotiating the benefits and the risks of AIacross different applications and publicsand finding new forms of settlement andnew narratives. It would help to ensurethat trustworthy information, in relevantand accessible forms, informed publicnarratives on AI across sectors andapplications. It would need to be able toreach into the forefront of research,development and innovation, such asthrough the Turing, and to connectpeople and information for discussionson opportunities, the distribution ofbenefits and of risks, shortcomings anduncertainties.

This kind of resource would in turnunderpin the regulatory infrastructurethat is outlined in the next section, byensuring that public debates wereconsistently well-informed.

18Welcoming world-class Turing AI Fellows to the Institute, The Alan Turing Institute (October 2019)19New Turing AI fellows to deliver world-class AI research, UKRI (November 2020)20Short, online courses can unblock gender and other diversity issues in digital skills education, Institute of Coding (September 2020)

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The UK should aspire to be the bestplace in the world to access and usesafe, secure and good quality data inorder to develop new applications andbusiness models.

The UK should also be the best place forthe public to exercise scrutiny of, andinput to, automated decision making, inorder to ensure the public can trust AI. Todo this, the infrastructure it will need isnot only physical, such as broadbandnetworks and high performancecomputing capacity, but also virtual andsocial, encompassing processes, people,standards, governance and practices. Asystemic reimagining of data availabilityand governance will be required acrossdisciplines to transform traditionalpractices and public acceptability.

As for other infrastructure assets ofnational importance, the government hasa key role in funding and providing data

infrastructure. An important componentto this should be in creating a cross-sectoral Information ManagementFramework that is AI-friendly anddesigned for clean, codified, real-timedata so that it forms the best possiblebasis for private and public sectorinnovation.

Trustworthy and accessible data

AI thrives on data, and makes newdemands on data collection, curation anduse. The government should focus itsplans to make more public sector datasafely and securely available, being clearabout which data will be increasinglyavailable, under what conditions and onwhat timescale. In the private sector,while regulators have begun good workto audit AI for personal data protectioncompliance21, more work is needed tohelp businesses seeking to use data forAI by creating the conditions for the

3. Data,Infrastructureand Public Trust

21

deployment of suitable privacy enhancingtechnologies.22 This should be furtheredby accelerating work on translating theintent of a data sharing agreement into anactionable legal framework, andestablishing guidelines for legalframeworks around different data sharingstructures such as trusts, cooperativesand contracts.

It will be important to note that whilesome AI systems will continue to rely onaccess to the largest possible data sets,volume should not be the only priority forschemes to promote access: some of thenext generation of AI technologies will bedesigned to work with much smaller datasets. This illustrates a more generalprinciple which is that the detailedinfrastructure needed to make AI work indifferent markets is often very context-specific, and the UK will need a NationalAI Strategy that links general principles toindustry-specific arrangements ofgovernance to enable innovation andgrowth. One need shared by manymarkets, however, is in establishing datainfrastructure to permit companies tosafely share and access data to developcommon AI-related products. The publicsector should lead the way with examplesof good data sharing practices.

Good AI research, development andinnovation relies on a bedrock of gooddata practices and there is much to do toimprove the current position. Thisincludes embedding data principles suchas FAIR (Findable, AccessibleInteroperable, Reusable), adopting open

licences where possible, working betterwith multi-modal data (big data, smalldata, noisy data, unstructured data) andenforcing an agreed supportingframework on what it means ethically andfor society to share data for researchpurposes, and who owns it.

Ultimately, developers of AI applicationsought to consider the opening andsharing of research, analytics and data atthe fundamental design stage. Thisshould include encouraging digitalresearch, development and innovationinfrastructure, software and tools to beopen and standardised; providing accessto large-scale compute and dataresources tuned to AI applications; andsupporting the development anddeployment of Trusted ResearchEnvironments, which enablescollaboration and data access acrossseveral organisations.

While previous work around bestpractices in data management and usehas been helpful in setting the directionof travel, there remains a gap betweenthe aspirations set out in these principlesand their implementation, creating anadditional barrier to the development ofeffective data access frameworks. Furtherwork is needed to build a commonlanguage amongst data practitioners,building understanding of the nature ofthe datasets being used and how best tomanage them. In many cases, data canoutlive the systems that collected it, andconscious investment in trustworthysystems that can outlive the lifecycle of

21Guidance on AI and Data Protection, ICO (2020)22Privacy Enhancing Technologies, The Royal Society (March 2019)

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our standard modes of datamanagement will reap many benefits.

The emergence of a small number of bigplayers in several digital markets hassparked a timely debate on the properrole of government and regulators inpreserving and enhancing competitionacross the digital economy. The role ofdata as a driver of concentration andpotential barrier to entry for smaller firmshas been a central theme of recentreviews.23 The government has alsorecognised the growing importance ofdata access and openness to promotingcompetition and innovation, havingestablished a dedicated Digital MarketsUnit to advise on the functions,processes and powers which may beneeded to address the challenges posedby fast-moving digital markets.24

Whatever the government’s final decision,clear and flexible regulation will be criticalto supporting good data practices, andcan help to level the playing field for newentrants into digital and data-drivenmarkets. There remains much to be donein interpreting existing data rules tosupport research, development andinnovation and well-resourced,independent regulators can assist inproviding guidance.

In addition to these systemic changes,the government might considerenhancing the nations’ approach to datagovernance: making sure data iscollected, used and shared in responsibleand trustworthy ways. Not appropriately

addressing data governance frameworkswould maintain a fundamental barrier tothe adoption and diffusion of AItechnologies and inhibit the quality ofwider sources of data. We recommend acoalition of institutions and experts worktogether to develop standard datasharing agreements compliant with dataprotection and intellectual property lawbut providing practical exemplars,especially for business-to-business datasharing that could be adapted andadopted across the economy. Without astrong system of data governance,existing tenuous public trust in data useand sharing will persist, impairing theUK’s long-term leadership ambitions.

Public trust and goodgovernance

Threaded throughout discussions aboutAI is the need for adaptive and informedregulation and standards as part of anenabling environment of goodgovernance, which includes publicengagement, transparency measures andresponsible research and innovation onthe part of businesses through thelifecycle of AI operations. Achieving sucha robust system of good governance iscrucial to building the public trust andconfidence in AI that will be essential toits uptake and long-term publicacceptability.

Recent events have demonstrated thatpublic and private sector AI andalgorithmic applications continue to beplagued by public scepticism and lack

DATA, INFRASTRUCTURE AND PUBLIC TRUST 23

widespread legitimacy. Developing anddeploying trustworthy AI will bedependent on the UK strengthening itsgoverning environment in a manner thatboth provides guidance and confidence tobusinesses to innovate and adopt AI, andreassures the public that the use of AI issafe, secure, fair, ethical and dulyoverseen by independent entities. This isbeginning to be addressed in the NationalData Strategy,25 but further explorationand scrutiny of appropriate governancemechanisms, particularly concerningpublic sector adoption of AI, is overdue.We highlight one example in this section -a public interest data bill - that may serveas a method of increasing accountabilitythrough public engagement.

It is clear that there remains afundamental mismatch between the logicof the market and the logic of the law.Technology markets extract value fromcollective data while laws respond toindividual opportunities and threats. Thishas a critical impact on the publicacceptability of data as infrastructure -data supported by people, processes andtechnology - and will only worsen if it isnot sufficiently addressed.

One method that could begin to combinenew forms of innovation andaccountability is to involve the public inconsidering new ways to complementindividual rights-based approaches suchas consent. These would include newways of ensuring public scrutiny ofautomated decision-making and the typesof transparency that lead to

accountability: revealing the purposes andtraining data behind algorithms, as well aslooking at their impacts. They wouldinclude public engagement, including inalgorithmic impact assessments. And theywould look to ensure that existingregulations and regulatory bodies had notonly the capacity, but also the capability tofully consider the implications of AI inareas such as labour, environmental, andcriminal law. These three tenets: (1) cleartransparency about automated decisionmaking, (2) the right to give meaningfulpublic input and (3) the ability to enforcesanctions could be encapsulated in aPublic Interest Data Bill.26

Fundamental to the development of AIsystems is access to data. The UK’sresponse to the COVID-19 pandemic hashighlighted gaps in the UK’s capabilities inthis space, in particular around thegovernance of ‘happenstance’ data,generated by individuals in the course oftheir daily activities.27 Trustworthy accessto such data can help generate insightsfor public policy and research, enablingthe government to better understand theimpact of its policy interventions inrapidly-evolving circumstances. Theirexpertise in the collection andmanagement of data means the Office forNational Statistics is well-placed to leadthe development of governanceframeworks for these new data sources.Government should enhance their role inthis respect through the National DataStrategy and associated policyframeworks.

23 ‘Unlocking digital competition’, Report of the Digital Competition Expert Panel, UK Government (2019)24New competition regime for tech giants to give consumers more choice and control over their data, and ensure businesses are fairly treated, UK Government (November 2020)25National Data Strategy, UK Government (September 2020). Mission 2 regards a pro-growth and trusted data regime, and Mission 3 concerns transforming the government'suse of data.26The Data Delusion: Protecting Individual Data Isn't Enough When The Harm is Collective, FSI Stanford (July 2020)27Data readiness: lessons from an emergency, DELVE Report No 7 (November 2020)

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Making the UK the best place in theworld to research and use AI requires itto be world-leading in the provision ofresponsive regulation and governance.While fundamental principles of goodgovernance may be fixed and applyacross sectors, the details will not, andthis means that the systems ofgovernance themselves need to be readyto respond and adapt more frequentlythan has typically been true of systems ofgovernance in the past. We thereforesuggest commissioning an independententity to provide recommendations onthe next steps in the evolution ofgovernance mechanisms, includingimpact and risk assessments, bestpractice principles, ethical processes andinstitutional mechanisms that willincrease and sustain public trust.

Public trust in the applications of AI andalgorithms depends on thetrustworthiness of these applications andthose that develop and implement these.Good governance also needs toencourage good culture amongst theindividuals and organisations that areresponsible for the development andapplication of advanced informationtechnology. At the same time, the UKneeds to continue investing in data-specific regulatory capacity andalgorithmic accountability, ensuring thatinstitutions are well funded to cover theadvisory, enforcement and regulatorylandscapes as sector digitalisationcontinues at pace. UK governance shouldcombine sector-specific expertise withcross-sector insights, bringing regulators

in different sectors together to connectwith each other and begin to experimentwith new ways of encouraging safe AIinnovation across and within their remit.

Digital Twins and Living Labs

Applying AI systems responsibly inevitablyrequires a great deal of developmentbefore getting anywhere near thephysical world. Digital twins (a realisticrepresentation of assets, processes,systems or institutions in the built,societal or natural environments) areincreasingly recognised as important inmany fields as they can provide insightinto how complex physical assets andcitizens behave in real time and can helporganisations improve decision-makingand optimising processes. An expandedprogramme to advance digital twincapability, with interoperable datastandards, could help developers explorethe potential for applications in thephysical world but cheaper, faster andwith less risk, as well as a betterunderstanding of the implications andimpacts to society and the economy. AIthrives when stitching digital twinstogether, using machine learningalgorithms to analyse complex patternsand make decisions between differentprocesses and systems alongsidegenerating new insights with newlyhybridised datasets.

The National Digital Twin Programme,currently being driven by the Centre forDigital Built Britain, aims to create anInformation Management Framework to

DATA, INFRASTRUCTURE AND PUBLIC TRUST 25

enable the building of digital twins acrossthe built environment. Building on thiswork, a more ambitious cross-sectoral UKDigital Twin programme, governed by theGemini Principles28, would be a moonshotchallenge and would encourage morewidespread use of open synthetic data todevelop AI applications in many specificpotential applications where the risks toindividuals or society might otherwise belarge. AI could also use these digital twinsto explore real world optimisations thatare beyond human scale.

In developing those AI applications and AImodels and testing with synthetic data,there comes a point when you must beginto model and rehearse in the real worldwith messy, unpredictable and diverseenvironments. To achieve this in a safeway without causing inadvertent harm,the UK needs to create a nationwidenetwork of living laboratories, physicalenvironments that start asrepresentations of the real world butmore constrained.

AI will provide the embedded intelligencewithin all manner of new smart machines,many with autonomous capabilities, thatcan work collaboratively with humans totransform their productivity, safety andquality of life. By enabling the fast andsafe development, testing anddemonstration of new AI-based goodsand services, including distributedsoftware systems and intelligenceembodied in smart machines andproducts, businesses, researchers and

citizens can gather the data andexperience necessary to inform futurepolicy, regulation, products and services.

A globally leading role

Finally, the UK should urgently andsustainably build upon its stronginternational position. It is one of the topthree countries in terms of researchpublications in AI.29 This strengthcombined with the UK’s diplomatic weightcan be a catalyst to shape internationaldiscussions, but our leading internationalposition is not guaranteed. We shouldtherefore strengthen existingpartnerships with other like-mindedcountries, such as France and Canada aswell as consider carefully how to increasebilateral cooperation with countries likethe US and China, such as through therecent declaration signed with the US toincrease collaboration on AI R&D,30 toenable effective strategic action to shapea global AI landscape. One immediate wayforward is to leverage our foundingmembership in the Global Partnership forAI (GPAI), a scalable club of like-mindedgovernments ready to be led by expertson tackling the most pressing prioritiesfor collaboration, where the UK is alreadyleading on data governance.31

The UK has a pivotal opportunity to playan influential role in the coming year as itadopts the Presidency of the G7 and the26th UN Climate Change conference.Over this period the UK should use thisopportunity to leverage global collective

28The Gemini Principles were developed by the Centre for Digital Built Britain to guide the responsible development of digital twins and a National Digital Twin for the public good.The Gemini Principles Centre for Digital Built Britain (2018)29Global AI Talent Report 2020, (October 2020); AI Research Rankings 2019, Medium (December 2019)30Declaration of the USA and the UK on Cooperation in AI Research and Development, UK Government (September 2020)31An open call for input from GPAI’s Data Governance Working Group, OECD (November 2020); we understand the recommendations from this working group will be incorporatedinto the National Data Strategy.

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intelligence to advise on the biggestchallenges AI presents and position theUK as a centre to operationaliserecommendations, including thoserelating to the UN’s SustainableDevelopment Goals.

Alongside global leadership on sharedchallenges, the UK ought to consider itsown strategic advantage as weendeavour to become less dependent oncritical technologies from non-alliedsources of supply. Advancements in AIproducts and services underpin thecapacity to compete economically but isalso the domain of contests betweenoverseas power bases and other nationstates, which are sometimes not wellaligned to the UK interest. One way torespond to this shift in geopoliticalcontext is to invest strategically inresearch, development and innovation,choosing areas of national strengths suchas AI and supporting them heavily withthe aim of becoming globally competitive.

If we align this strategic investment withthe construction of broad coalitions ofinterest on the global stage, the UK canincrease our sovereign capabilities andattract researchers and technologistswith the skills to develop leadingproducts in a number of competitivesectors. Government should alsoconsider how to redefine procurementand intellectual property to encourage acreative technology ecosystem to achievethese aims.

DATA, INFRASTRUCTURE AND PUBLIC TRUST 27

28 AI ROADMAP

The preceding sections outline thepillars that would provide the basis forsustained uptake of AI across thecountry and the economy as a whole.

This section proposes transitional andother more specific measures to boostinitial take up and to evolve existingmarkets in the crucial areas of businessadoption, startup & scale-up support,public sector adoption, health and socialcare, climate change, and defence.

Business as smart adopters

Across society and the economy, andsince the first weeks of 2020, the COVID-19 pandemic has accelerated theadoption of digital systems such as e-commerce platforms and marketplaces,and tools to enable remote working. TheUK therefore has an opportunity to buildon this momentum to showcase andreward the uptake of AI by encouraging

further investment and enablingbusiness, especially SMEs, to be smartconsumers for AI systems.

Creating confidence so that businesscustomers increasingly seek out AIsolutions, knowing that they will be ableto get sound advice and robust products,is crucial. There is further need to buildon grants to access impartial advice onhow to use digital and AI solutions tohelp the growth of SMEs that have beenexacerbated by the COVID crisis.Scotland’s Data Lab is a good example ofan initiative that fosters trustedcollaboration between industry and AIexpertise, having invested in 120Collaborative Innovation Projects atapproximately £12 million value over thelast 5 years, and recently launched aTorch Business Advisory Service tosupport SMEs on their data journey.

4. National,Cross-sectorAdoption

29

The largest companies that are able torealise the potential of AI throughbusiness transformation find themselvesincreasing productivity, reducing costsand innovating faster to remaincompetitive. By ensuring the enablingfoundations described in the first 10recommendations are in place, the UKcan also be a fertile ground for its existingFTSE 100 and the future 100.

Supporting high growth AIstartups

As these businesses initiatives stimulatedemand for AI products, we need toensure that the AI startup and scaleupvendor ecosystem thrives. Supporting AI-first businesses by providing thenecessary physical infrastructure to offerservices to the largest and smallest localand multinational companies would helpthese businesses scale, develop andbecome the next multi billion dollarcompanies.

The government should encourage moreinitiatives like Tech Nation’s Applied AIProgram32 to directly support smallerstartups to grow across the UK. Thecommunity welcomed an additional £200million from the British Business Bankwhich increased VC funding to highgrowth companies,33 and there is acompelling argument to increase thisfunding, or alternatively to consider publicinvestment in promising companies thatseek to advance government priorities ina similar style to the National SecurityStrategic Investment Fund. For scaleups,

the funding gap that exists at series B+means that AI companies struggle to stayunder UK ownership. Government mayconsider supporting at this stage toensure companies can take foreigninvestments in these rounds while alsostaying in the UK as they grow.

The UK government needs to assiststartups and scaleups to compete mosteffectively for the best talent. The StudyVisa program may need to be extendednow we have left the European Union. Tothis end, there is a prominent role for thegovernment to position the UK as the go-to AI startup location in the region. It’simportant to spread the message that theUK has more capital and talent thananywhere else34 and a unique proximity ofbuyer to vendor as well as being far easierto incorporate and run a business herethan in Europe.

To increase the number of SMEs andbuild the right entrepreneurialinfrastructure for AI-first companies, it isalso worth exploring the possibility ofbuilding on Innovate UK’s KnowledgeTransfer Partnerships (KTPs), in which thegovernment could introduce a series ofAI-specific KTPs to stimulate market readyAI research in areas of direct relevance tobusinesses to commercialise research.

The UK currently lacks a prominent cohortof professionals who understand thecomplex sales and leadership skillsneeded to build, articulate and sell AItools and develop leaders of AI researchlabs, and there is concurrently a specific

32Applied AI, Tech Nation (2020)33Extra £200 million backing for British businesses, British Business Bank (April 2019)34Tech Nation Impact Report, Tech Nation (2020); UK Tech For A Changing World, Tech Nation (2020)

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demand to increase the number ofskilled product managers in AI. Werecommend a marketing campaign andprograms to encourage internationalproduct managers to come to the UK,and for UK citizens to focus theireducation or re-skilling in this direction.This would help turbo charge the UK’sability to effectively commercialise theworld class research coming out of ouruniversities.

Enabling public sector adoption

The UK public sector will be a priorityarea when it comes to AI innovation.Improving policy-making processes (forexample, ensuring it is more resilient toshocks to the system), and the provisionof more efficient and effective publicservices (for example, using data-driven‘real-time’ economic forecasts), built onstrong ethical foundations, will becomeessential to improve the security andwell-being of citizens.

There is an urgent need to have a digitallyand data enabled local and nationalgovernment. To leverage theopportunities of AI in the public sector,there will need to be new ways ofensuring that data can be accessed andevaluated across public sectororganisations. The government shouldensure there is a central place for civilservants to access information about allof the AI-based tools and projects beingundertaken across departments. AIshould be used in projects that areoutcome-based and only where

absolutely appropriate, and departmentsshould learn rapidly from each otherabout what works well and what needs toevolve. In time, the focus should be onlinking public sector data with privatesector data, rapidly enhancing theinsights that can be gained throughdiverse datasets and a moresophisticated adoption of AI.35

Government should fund interdisciplinarypathfinder projects – requiringcollaboration between industries andacross government departments, withdifferent academic disciplines – as ameans of demonstrating the potential ofAI technologies to support pathfinderprojects and building capability in the civilservice to deliver such projects.Importantly, the application of AI in thepublic sector should not fall explicitly onone department to lead. It may be that aChief Information Officer embedded inthe heart of government would work wellto provide the strategic direction andleadership required for such a multi-stakeholder task.

The huge benefits of data sharing will beworth little unless governmentdepartments are enabled to intelligentlyprocure products that suit differentcontexts and services. More work isneeded to demystify algorithmic decisionmaking in government, with trainingprograms for officials so they can suitablyassess AI systems for themselves and seehow it would be deployed as part of anincreasingly efficient civil service. TheONS is working towards a target of 500

NATIONAL, CROSS-SECTOR ADOPTION 31

data analysts across government trainedin data science techniques this year,36 andto enable a more thorough upskilling of AIand machine learning analysis, workshould intertwine with the Turing, theRoyal Society, the Royal Statistical Societyand others in determining how datascience and AI practitioners may beprofessionally standardised.

Public sector bodies wishing to adopt AIoften face challenges in buildingprocurement capability. Governmentshave tried to enable public procurementto better support innovative goods andservices, while ensuring value for moneyand relatively low risk. This governmentshould continue to build on initiatives thathave worked, such as GovTech and theguidelines for AI procurement,37 to allowthe public sector to effectively purchaseemerging tech such as AI. Greater AIadoption in the public sector throughpublic sector procurement createsmarkets for data science and AItechnologies to be developed, tested anddeployed by government departments, toenable a pull-through of innovation fromresearch, often via SMEs.

It is important to note that evidenceshows the human rights of the poorestand most vulnerable are especially at riskwhen public services are automated.38 Assuch we suggest this work is donecarefully and with independent ethics

committees tasked to consider ethics,including social and economic impacts.

Health and social care

The demands on healthcare are changingrapidly as populations drift, age, and theirneeds evolve, and with a huge reductionin the cost of measurement. With thisubiquity of such data, informed andimproved interventions throughprioritisation and personalisation canmaximise benefit.

Given the social and economicimportance of health and the UK’spotential to build on market opportunitiescreated by the NHS, the sector has been,and should continue to be, at theforefront of AI adoption. The importanceof the link between health, mental healthand social care has been especially clearduring the COVID-19 pandemic, and wehave also seen areas in which AI couldhave been better prepared to deal withthat link if we had considered the promiseof AI in this sector earlier. AI can helpmanage the complex information flowthat is needed for the integration of thesesystems. NHSX, in delivering the HealthSecretary’s Tech Vision39, is taking the leadin unblocking issues including (but notlimited to) governance, data access andprotection, and inclusion. The Council,who are supporting NHSX with their AILab40 and AI Awards,41 looks to NHSX towork with others across the research,

35There is a fundamental need to consider transparency and data bias as public sector adoption increases, taking into account analysis and recommendations from, amongothers: Review on Artificial Intelligence and Public Standards, The Committee on Standards in Public Life (February 2020); CDEI review into bias in algorithmic decision-making,Centre for Data Ethics and Innovation (November 2020)36Looking to the future at the Data Science Campus, Office for National Statistics (March 2019)37Guidelines for AI Procurement, UK Government (June 2020). Developed in collaboration with the World Economic Forum Centre for the Fourth Industrial Revolution.38Statement on the Visit to the United Kingdom, by Professor Philip Alston, United Nations Special Rapporteur on extreme poverty and human rights, United Nations HumanRights Office of the High Commissioner (November 2018)39The future of healthcare: our vision for digital, data and technology in health and care, UK Government (October 2018)40The NHS AI Lab, NHSX (2020)41Artificial Intelligence in Health and Care Award, NHSX (November 2020)

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development and innovation landscapeto continue building towards theseopportunities and developing bestpractice guidance for AI adoption in thehealthcare sector.

As the adoption of AI increases outside ofthe hospital setting, so too will the‘datafication’ of networked digitaltechnologies which create new healthinsights for individuals. This carriesabundant opportunities for thepreventative health of citizens, forexample in stimulating new and moreefficient ways to diagnose and preventdisease beyond clinical boundaries, butthere are also significant concerns if notgoverned correctly. Individuals’ healthquickly becomes legible to actors outsidemedical and clinical settings, for exampleinferred sensitive personal informationheld by online platforms, and appropriateoversight ought to be considered in howthese technologies develop.

We also encourage further developmentin areas with others in the sector wherethe UK can build a competitiveadvantage. Three particular areas standout: the first is in creating a data strategyfor health and care, which achieves clean,codified and real-time data with a strongsectoral data governance framework. Thesecond is the opportunity to develop newmodels of partnership and incentives forpartnerships between SMEs and the NHSthat flourish and scale beyond ideation.The third opportunity would be to createa better understanding of the skills andknowledge required for citizens and

healthcare professionals living andworking with AI technologies, buildingespecially on the Topol review42 and NHSDigital Academy.

Finally, building on the UK’s world-leadinglife sciences industry and increasinglydigitally mature NHS, there is anopportunity to establish the UK as thego-to-place for biomedical research. Withthe appropriate governance in place, andthrough closer collaboration of the lifesciences industry and the NHS, AI has thepotential to empower large national trialsmore efficiently, including through theseamless adaptation of subjects totreatments.

Climate change

AI technologies are an essential part ofthe toolbox for innovating to reducegreenhouse gases in the atmosphere,and to reduce the environmental impactsof goods, services and human activities.We are already seeing AI contributing to agreater grasp of complex environmentaland sustainability systems, fromforecasting supply and demand at real-time43 to combating illegal deforestation44

and understanding Arctic sea ice loss.45

One way to accelerate the use of AI forthese purposes would be to build it intorelevant moonshots, such as that on newmaterials for energy storage andrenewables and create incentives for AIcompanies to address these and otherNet Zero challenges.

NATIONAL, CROSS-SECTOR ADOPTION 33

The challenges to increased adoption ofAI in related sectors, particularly in energy,has been explored elsewhere46 but echosimilar constraints in other sectors,notably in the accessibility, interoperabilityand labelling of datasets that cancontribute to cross-sector cooperation onenergy and climate initiatives. The realityis also that the continued development ofdigital technologies, including AI andcomplementary technologies such as 5Gand the Internet of Things, create anenvironmental impact that also needs tobe monitored.

As a part of the UK’s globally leading rolein AI, there is an opportunity to carve outa leading path on the global stage whereothers see a similar need to increase therole of AI in energy and climate for publicgood. The UK should consider drawing upa plan that ensures responsible AI issuitable for the planet and helps buildbroader awareness, inform regulation,and incentivise researchers anddevelopers to make more conscious andaccountable decisions in these areas.Ahead of COP26, the UK should considerhow to develop and showcase the UK’sability in cleantech, which spans the entirecountry and represents a successful,innovative path to Net Zero,47 and use itsspotlight to encourage an open,international digital ecosystem of data,algorithms and insights that can generate

reliable information in real-time about thestate of the environment and interactionsbetween the economy, society and theenvironment.

Defence and security

AI is central to a broad range of defenceand security applications, which arebecoming increasingly data rich andnetworked. They require multiple sourcesof information to be brought together tounderstand a rapidly evolving situation, todetermine a timely course of action andto oversee the enactment of theresponse. The information involved isoften multi-modal (text, audio and video)and is invariably uncertain, incompleteand contradictory. Sometimes it is alsointentionally misleading. AI methods arenecessary to make sense of this volume,variety and velocity of information.However, further developments arerequired to scale the underpinning fusiontechniques, to have them work in closerpartnership with the humans involved,and to ensure the ensuing decisions andplans can be understood and justified.

The increased connectivity of all aspectsof our society means that digital assetsare increasingly common, and valuableand physical assets are increasinglyaccessible remotely. These bringtremendous benefits, but also open up

42The Topol Review advised the government on implementing technologies such as AI in the NHS and to enable NHS staff to make the most of them to improve services.The Topol Review, NHS (February 2019)43Solar nowcasting with machine vision, The Alan Turing Institute (current research)44The fight against illegal deforestation with TensorFlow, Google (March 2018)45Understanding Arctic sea ice loss, The Alan Turing Institute (current research)46AI Barometer Report, Centre for Data Ethics and Innovation (June 2020)47UK Tech For A Changing World, Tech Nation (2020)

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opportunities for interference anddisruption, leading to a requirement for asecure, connected intelligence. AItechniques are necessary to monitor thecomplexity of these networked systemsand to determine when they areoperating normally and when they arenot. As cyber-attacks becomeincreasingly sophisticated, AI is needed todetect and respond to them.

We welcome the commitment to create anew MOD agency for AI, and look forwardto further detail in the Integrated Reviewin 2021.

We are now living in a challengingeconomic and geopolitical environmentin which global tech companies holdsway over national strategic advantages.By strengthening our ties with like-minded nations, the UK can increase ourcapabilities in co-creating technologicaldevelopments, drawing innovations fromoblique application areas and developingleading products which are then adoptedby our allies. The broader AI communitywill be required to engage in this spaceas the security sector becomes morepervasive, and the government will needto find ways of working acrossdemanding security frameworks toenable wider collaboration.

Part of opening up wider collaboration onthis front will require new levels ofinternationally competitive investment inadvanced hardware technologies thatserve the UK ecosystem, as the drivingforce behind fast and secure innovation.

Co-creation – at scale – of technologicaldevelopments in an application agnosticenvironment (benefiting from innovationsfrom other application areas) could be apotential model. This would also provideadditional benefits to improvedtechnological export capabilities forBritain, and further benefits to attractingworld-class technologists and innovatorsin this space, not seen in Britain since the1980s.

NATIONAL, CROSS-SECTOR ADOPTION 35

36 AI ROADMAP

AI Council

Sir Adrian Smith FRSInstitute Director and Chief Executive,The Alan Turing InstitutePresident Elect of The Royal Society

Alice Bentinck MBECo-founder, Entrepreneur First

Ann CairnsExecutive Vice-Chairman, MasterCard

Christopher Bishop FRS FRSE FREngLaboratory Director,Microsoft Research CambridgeProfessor of Computer Science,University of Edinburgh

Claire Craig CBEProvost, The Queen’s College Oxford

David Lane CBE, PhD FREng FRSEProfessor and Founding Director,Edinburgh Centre for Robotics

Kriti SharmaFounder, AI for Good

Máire O'Neill FREngProfessor, Centre for Secure InformationTechnologies, Queen's University Belfast

Marc WarnerChief Executive Officer, Faculty

Sir Mark Walport FRS FRCP FRCPathFMedSci FRSEChief Executive, UK Research andInnovation, 2017 to 2020

Martin TisnéManaging Director, Luminate

Neil LawrenceDeepMind Professor of Machine Learning,University of Cambridge

Nick Jennings CB, FREngVice-Provost for Research and Enterprise,Imperial College LondonProfessor, Artificial Intelligence,Imperial College London

Dame Patricia Hodgson DBEMember of the Board,Centre for Data Ethics and Innovation

Paul Clarke CBEChief Technology Officer, Ocado Group2012 to 2020

Pete BurnapProfessor of Data Science & Cybersecurity,Cardiff University

Priya Lakhani OBEFounder and CEO, CENTURY Tech

Rachel DunscombeCEO, NHS Digital AcademyVisiting Professor, Imperial College London

Tabitha GoldstaubCo-founder, CognitionXUK AI Business ChampionChair, AI Council

Dame Wendy Hall DBE, FRS FREngRegius Professor of Computer Science,University of SouthamptonUK AI Skills Champion

Acknowledgements

37

In October 2019 the Council put out a call forany interested individuals to be part of a widerecosystem, to contribute to the Council’s work.Over 450 people responded, and the Council isgrateful to them for their contributions over thelast few months, including for their views on thisRoadmap. This includes:

Adrian Joseph OBE, Managing Director,Group AI and Data Solutions, BT

Adrian Weller, Program Director for AI,The Alan Turing Institute

Alexandra Mousavizadeh, Director,Tortoise Intelligence team

Andrew Hopper CBE FRS FIET FREng,Vice-President, The Royal Society

Brent Hoberman CBE, Executive Chairman andCo-founder, Founders Factory

Carly Kind, Director, Ada Lovelace Institute

Demis Hassabis CBE FRS FREng FRSA,CEO and co-founder, DeepMind

Frank Kelly CBE, Professor of the Mathematics ofSystems, University of Cambridge

Gerard Grech, CEO, Tech Nation

Gillian Docherty OBE, CEO, The Data Lab Scotland

Hayaatun Sillem CBE FIET, CEO,Royal Academy of Engineering

Sir Ian Diamond FBA FRSE FAcSS DL,UK National Statistician

Indra Joshi, Director of Artificial Intelligence, NHSX

Jeni Tennison OBE, Vice-President,Open Data Institute

John Spindler, CEO, Capital Enterprise

Julia Adamson, Director of Education,British Computer Society

Lila Ibrahim, COO, DeepMind

Professor Dame Lynn Gladden DBE FRS FRSCFInstP FREng, Executive Chair, EPSRC

Matthew Taylor CBE FAcSS, CEO, The RoyalSociety for the Encouragement of Arts,Manufactures and Commerce

Professor Dame Ottoline Leyser DBE FRS,Chief Executive, UK Research and Innovation

Sir Patrick Vallance FRS FMedSci FRCP,UK Government’s Chief Scientific Adviser

Philip Colligan, CEO, Raspberry Pi

Roger Taylor, Chair, Centre for Data Ethicsand Innovation

Saul Klein, Co-founder, LocalGlobe

Simon McDougall, Director, Technology &Innovation, Information Commissioner’s Office

Simon Peyton Jones FRS MAE, Chair,National Centre for Computing Education

Stan Boland, CEO, FiveAI

Stian Westlake, CEO, Royal Statistical Society

Stuart Russell, Professor of Computer Science,University of California, Berkeley

Lord Tim Clement Jones CBE FRSA, Co-chair,AI All-Party Parliamentary Group

Vanessa Lawrence CB HonFREng FRICS,Non-Exec Director, Satellite ApplicationsCatapult and Non-Exec Director and Trustee,The Alan Turing Institute

Zoubin Ghahramani FRS,Professor of Information Engineering,University of Cambridge

Wider AI ecosystem

BLANK PAGE

40 AI ROADMAPAbout the AI CouncilThe AI Council, an independent expert committee, provides advice to theUK Government and high-level leadership of the Artificial Intelligence ecosystem.

The Office for Artificial Intelligence (a joint BEIS-DCMS unit) is secretariat tothe AI Council.

www.gov.uk/government/groups/ai-council

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Published January 2021