RESEARCH
4.0
RESEARCH IN
THE AGE OF
AUT MATIONPROFESSOR ROB PROCTERBEN GLOVERELLIOT JONESSEPTEMBER 2020

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CONTENTS
ACKNOWLEDGEMENTS

PAGE 4EXECUTIVE SUMMARY

PAGE 5INTRODUCTION

PAGE 9CHAPTER 1
THE FRONTIER OF
AI-ENABLED RESEARCH

PAGE 12

CHAPTER 2
SCENARIOS FOR THE
FUTURE OF RESEARCH

PAGE 22

CHAPTER 3
A POLICY AGENDA
FOR RESEARCH 4.0

PAGE 28

3

ACKNOWLEDGEMENTS
We would first like to thank Jisc for supporting uswith this project.
We would like to thank Rob Procter for agreeing
to partner with us for this report and for all his
invaluable advice, guidance and challengethroughout the project.
We would like to thank the academics who took thetime to share their thoughts and experiences with us
at a workshop held at the University of Salford andeveryone who gave up their afternoon to join us forour policy roundtable.
We are also extremely grateful to all those that gaveup their time to be interviewed by us. Without themthis report would not have been possible.
At Demos we would like to thank all the participantsof our scenario planning workshop. In particular,
we would like to thank Stanley Phillipson Brown
for his excellent work assisting with research duties
for this report.
Ben Glover
Elliot Jones
September 2020

Rob Procter is Professor of Social Informatics in the department ofComputer Science, Warwick University. He is also a Faculty Fellow
at The Alan Turing Institute. Previously he has held positions at
Manchester and Edinburgh universities. His research interests arestrongly interdisciplinary, and focus on understanding how individual,organisational and social factors shape processes of design,development and adoption of digital innovations. Current interests
include social media analytics and social data science.
Ben Glover is Deputy Research Director at Demos.
Elliot Jones is a researcher at the Ada Lovelace Instituteand was a researcher at Demos.

4

EXECUTIVE
SUMMARY
There is a growing consensus that we are at thestart of a fourth industrial revolution, driven by
developments in Artificial Intelligence, machinelearning, robotics, the Internet of Things, 3-D
printing, nanotechnology, biotechnology, 5G, newforms of energy storage and quantum computing.
This wave of technical innovations is already having
a significant impact on how research is conducted,
with dramatic change across research methods inrecent years within some disciplines, as this project’s
interim report set out.1
Whilst there are a wide range of technologies
associated with the fourth industrial revolution,
this report primarily seeks to understand what
impact Artificial Intelligence (AI) is having on the
UK’s research sector and what implications it hasfor its future, with a particular focus on academic
research. Following Hall and Pesenti in their recent
government review of the UK’s AI industry, we
adopt the following definition:“[AI is] an umbrella term to cover a set ofcomplementary techniques that have developed
from statistics, computer science and cognitive
psychology. While recognising distinctions
between specific technologies and terms (e.g.,
artificial intelligence vs. machine learning,machine learning vs. deep learning), it is useful
to see these technologies as a group, when
considering how to support development anduse of them”.2
Hence, we will use ‘AI’ as an umbrella term
throughout the report to cover a range of different
technologies (e.g., machine learning, datavisualisation, robotics).3

1.
2.
3.

Building on our interim report, we find that AI isincreasingly deployed in academic research in the
UK in a broad range of disciplines. The combinationof an explosion of new digital data sources
with powerful new analytical tools represents a‘double dividend’ for researchers. This is allowing
researchers to investigate questions that would
have been unanswerable just a decade ago.
Whilst there has been considerable take-up of AI in
academic research, steps could be taken to ensure
even wider adoption of these new techniques
and technologies, including wider training in
the necessary skills for effective utilisation of AI,
faster routes to culture change and greater multidisciplinary collaboration.
We also envisage a range of possible scenarios
for the future of UK academic research as a resultof widespread use of AI. Steps should be taken tosteer us towards desirable futures. The researchsector is not set in stone; it can and must be shapedby wider society for the good of all. We consider
how to achieve this in our recommendations below.
We recognise that the Covid-19 pandemic means
universities are currently facing significant pressures,with considerable demands on their resources
whilst simultaneously facing threats to income. As
a result, we acknowledge that most in the sector
will be focused on fighting this immediate threat
instead of thinking about the long-term future ofresearch. But as we emerge from the current crisis,
we urge policy makers and universities to consider
our recommendations and take steps to fortify the
UK’s position as a place of world-leading research.
Indeed, the current crisis has only reminded us of
the critical importance of a highly functioning andflourishing research sector.

Jones, E., Kalantery, N., Glover, B.. Research 4.0 - Interim Report. Demos, 2019.
Available at https://demos.co.uk/wp-content/uploads/2019/10/Jisc-OCT-2019-2.pdf [accessed 15 July 2020]
Hall, W., Pesenti, J. Growing the artificial intelligence industry in the UK. Available at https://assets.publishing.service.gov.uk/
government/uploads/system/uploads/attachment_data/file/652097/Growing_the_artificial_intelligence_industry_in_the_UK.pdf [accessed 15 July 2020]
Hall and Pesenti. Growing the artificial intelligence industry.

5KEY FINDINGSHow is AI changing academic research methods
in the UK?
We conducted a series of interviews with leading
UK researchers that use AI in their research.
Building on our interim report, we find that AI isincreasingly being deployed across UK universities
and in different disciplines, from STEM subjects tosocial sciences, the arts to humanities.
An explosion of new digital data sources andthe ability to extract more data from existingsources has vastly increased the available data forresearchers across a wide range of disciplines.
Once data is prepared for analysis, powerful new
analytical tools are driving further breakthroughsand discoveries. This is AI’s ‘double dividend’for researchers: new digital data and new waysof analysing those data, allowing researchers to
ask questions that would have been impossible adecade ago.
AI as it is currently deployed in academic research
was generally not viewed as freeing up time formore theorising, a hypothesis we flagged in ourinterim report and were interested in investigating.
This was because the use of AI in research is often
extremely time-intensive, due to the amount of
preparation and cleaning time of data and often
frequent experimental iterations involved to findthe best ‘solution’.
How is AI changing research processes andresearch administration in the UK universities?
We also explored in our interviews how AI is –or could be – used throughout the archetypal
research project lifecycle (e.g., literature reviews,
writing proposals, analysing data, writing
papers for peer review, etc.) and in researchadministration (e.g., reviewing papers and research
proposals, specification and management ofresearch programmes, etc.). The evidence from
our interviews suggests that there is relatively
little explicit adoption of AI to support the wider
research process and research ecosystem in UK
academic research.
For example, it is widely recognised that the peer
review process has been struggling for some years
to keep pace with the numbers of papers submitted
for publication. However, AI was generally viewed
as poorly suited to solving this problem. Most of

the issues relating to peer review were perceived tobe due to cultural and social factors that could not
be addressed in this way without introducing new
problems that might undermine confidence in theprocess.
However, there was a recognition amongst someinterviewees that the literature review stage ofthe research process could be aided by the use ofAI, though this does not appear to be happening
explicitly at present.
How is AI changing the wider academic researchecosystem in the UK?
Our interviews also explored how the use of AI in
academic research is changing the UK’s academic
research ecosystem. This allowed us to better
understand the financial, institutional and culturalbarriers to the further adoption of AI within
universities.
Interviewees were generally not concerned thatthe use of AI will negatively impact early career
researchers’ prospects by, for example, automatingsome of the tasks normally performed by early
career researchers. This is because its application isoften highly labour intensive. As a result, there areoften more tasks for early career researchers as aresult of using AI in academic research, not fewer.
However, there are concerns that researchers
are not receiving appropriate recognition forthese tasks (e.g. data cleaning, data annotationand curation, model building, etc.). Appropriate
recognition could include ensuring that the creationof re-usable datasets is properly credited in journal
articles that utilise their data, for example.
The capacity of digital infrastructure in UK
universities also appears to vary significantly.
Researchers in different departments, universities
and regions often have different experiences
accessing the infrastructure they need to conduct
research. This suggests that any attempt to improve
national digital research infrastructure must be
informed by a rigorous assessment of where theproblem lies, so support can be targeted where
it is most needed. In particular, those working in
the arts and humanities appear to face significant
challenges accessing the technical infrastructure
they would need in order to innovate their researchmethods.
Interviewees described how academic researchers
often lack the necessary skills to make full use of6

AI. We heard how humanities researchers often
lack sufficient quantitative and/or digital skills
and that appropriate utilisation of AI requires
domain knowledge and technical know-how, amulti-disciplinary combination that may be hard
to find. We also heard that too often those withthe technical skills lack a proper awareness ofthe ethical risks posed by AI. This is an evolving
problem; for example, risks relating to privacy
breaches increase with the linking of datasets,
and possibilities of introducing bias into decisionmaking processes increase as we rely more on AI
trained on datasets that themselves contain hidden
biases.
Successful utilisation of AI is also likely to demand
multidisciplinary expertise and working. Whilst there
are numerous strong examples of multidisciplinary
academic research applying AI in the UK,interviewees described that there are often barriers
to this way of working. These barriers are likely to
be challenging to overcome given they often stemfrom deep-rooted structural factors, such as the way
research funding is organised and, relatedly, thestructure and nature of academic disciplines.
Interviewees widely described how AI talent isbeing lost from academia to the private sector andoften does not return. Whilst this is partly due toa significant pay differential – owing to the widely
recognised global shortage of AI skills – it may also
be because returning to academia from industry
can be challenging for non-financial reasons. For
example, academic roles typically demand a strong
journal publication record that is sometimes difficult
to acquire when working in the private sector. Stepsneed to be taken to facilitate greater movement ofskilled people between academia and the private
sector.
How will AI impact the UK research’s sector
in the future?
We undertook a scenario planning exercise tohelp understand how the rise of AI will continue toshape the UK’s research sector in the future. Insteadof making precise predictions about the future,
which is fraught with difficulty and too often highly
inaccurate, we used scenario planning to produce
several distinct potential visions of the future of UK

research as impacted by the AI and the technicalinnovations associated with ‘fourth industrial
revolution’.
Using a three-stage process, we developed fivepossible futures for the UK research sector in
2040 as a result of the fourth industrial revolution.
Summaries of these scenarios are provided
below. More detail about the scenarios and themethodology used to devise them can be found in
Chapter Two.
Decentralised Research• Technological developments allow for a
democratisation and decentralisation of science,with independent researchers having access tothe same tools as those working in universities
and large companies. This allows for more
fluid research, with collaborations more easily
springing up between the public sector, private
sector, communities and citizens, with the state
enabling this through less hierarchical researchfunding and open access to research for all.
National Champions• The state works in concert with homegrown, UK
technology companies to push the frontiers ofscience, creating a nexus where the public andprivate sectors are co-equal partners in research.
The state funds and protects these companies
from outside competitors, be it from the US,
China, or Europe, and, in exchange, shares intheir successes and has a seat at the table in theirdecision-making.
Public Service Science• A state-driven research sector, directed bygovernment missions with the government
providing vastly increased public researchfunding, public service cloud computing facilities
and experimenting with new research models,e.g., in the mould of the Advanced Research
Projects Agency in the US (ARPA). However, thecountry is more closed off to the rest of the world
and research is focused primarily on serving theUK, not on serving other countries or the pursuitof knowledge.

7

Big Tech Research• The UK’s research sector is now dominated bylarge technology companies, most of them basedin the U.S. and China. UK academic research,where it continues to exist, does so in explicit
partnership with technology companies, whoprovide most of the funding. Whilst the UKcontinues to produce world-leading research,much of this is behind closed doors, hamperingscientific breakthroughs.
Backwater Britain• The UK’s research sector has stagnated and isentering a period of terminal decline. A failure
to invest in the right technologies, infrastructureand skills means that the UK has not been able
to keep hold of its position as a world-leading
country for research. The decline of the researchsector affects the wider economy, given therelationship between research and innovation.
The UK is viewed as a less attractive place toinvest and do business; its decline is symptomaticof a wider economic malaise.
RECOMMENDATIONS
Education and Skills

work early career researchers undertake will beappropriately recognised.
Infrastructure
To level up infrastructure provision to ensure that
researchers across the UK in all institutions canaccess fast, secure and reliable digital infrastructure,
we recommend that:
Recommendation 4: Infrastructure. A UK-wide
audit of research computing and data infrastructure
provision is conducted to consider how accessmight be levelled up.
Research Funding and Investment
To encourage greater uptake of AI within
universities, we recommend that:
Recommendation 5: AI Incentives. UK Researchand Innovation (UKRI) should consider incentivising
institutions to utilise AI wherever it can offer
benefits to the economy and society in their future
spending on research and development.
Strong interdisciplinary working is critical toeffectively utilising AI in research. To encourage
more interdisciplinary research in UK universities werecommend that:

To ensure that UK universities and the researchsector have the skills needed to fully and safely
harness the potential of AI, we propose some key
ideas that could deliver change. We recommend
these ideas are explored further by the sector,
along with the full range of ‘4.0’ technologies:

Recommendation 6: Interdisciplinary/Multidisciplinary Research. UKRI should
devote more funding to interdisciplinary andmultidisciplinary research programmes, such as theStrategic Priorities Fund.

Recommendation 1: Skills. The current post-16
curriculum should be reviewed to ensure all pupils
receive a grounding in basic digital, quantitativeand ethical skills necessary to ensure the effective
and appropriate utilisation of AI.

To encourage greater movement between
academia and industry, we recommend that:

Recommendation 2: Ethics. Universities should
ensure undergraduate and postgraduate courses inAI embed a ‘Responsible Research and Innovation’approach in the curricula to anticipate the negative
impacts of AI and designing methods to avoid or
mitigate them.
To ensure that researchers working with AI receive
the recognition they deserve, we recommend that:

Universities and the private sector

Recommendation 7: Researcher Mobility.
Universities should take steps to ensure that it iseasier for researchers to move between academia
and industry, for example, by putting less emphasis
on publications, and recognise other outputs andmeasures of achievement when hiring for academic
posts.
Recommendation 8: AI Fellowships. UKRI
should create and fund a number of ‘AI industry
fellowships’ at UK universities.

Recommendation 3: Early Career Researchers.
UK research funders should require research
proposals to make a clear statement that the8INTRODUCTION
A decade ago, Demos, supported by Jisc,
published The Edgeless University.4 Then, we
looked at how the internet, social networks andonline collaborative tools were set to transformthe creation of, and access to, knowledge. Today,
Demos is again looking at how technology istransforming academia, this time examining how
the fourth industrial revolution and AI, in particular,
are reshaping the research landscape.
DEFINITIONS
Whilst there are no universally agreed definitionsof AI, it is important to be clear about which
definitions we are following in this report. The
Engineering and Physical Science Research Council
uses this definition:“Artificial Intelligence technologies aim to
reproduce or surpass abilities (in computational
systems) that would require ‘intelligence’ ifhumans were to perform them. These include:
learning and adaptation; sensory understanding
and interaction; reasoning and planning;
optimisation of procedures and parameters;
autonomy; creativity; and extracting
knowledge and predictions from large,
diverse digital data.” 5
Following Hall and Pesenti, throughout this report
we will use the term AI as “an umbrella term tocover a set of complementary techniques that havedeveloped from statistics, computer science andcognitive psychology. While recognising distinctions
between specific technologies and terms (e.g.,
artificial intelligence vs. machine learning, machinelearning vs. deep learning), it is useful to see thesetechnologies as a group, when considering how
to support development and use of them”.6 Moredetail about different AI technologies that areincluded in our umbrella term is given below.

• Machine learning
Machine learning “...allows computer systems
to learn directly from examples, data, andexperience. Through enabling computers to
perform specific tasks intelligently, machinelearning systems can carry out complex processes
by learning from data, rather than following preprogrammed rules.” 7
Significant developments in machine learningin recent years (see below) are one of the key
drivers of the recent resurgence of interest in AI.
We now often interact with machine learning on adaily basis, from voice recognition used by virtual
personal assistants to the recommendations
tailored to us when we shop online.
• Deep learning
Deep learning is a family of powerful machinelearning techniques based on Artificial Neural
Networks (a model of computation inspired bybiological neural networks). These techniques
have achieved “state-of-the-art results in mostmachine learning tasks since their development”.8• Natural Language Processing
Natural Language Processing (NLP) uses machinelearning techniques to extract informationfrom unstructured data.9 Applications include‘chatbots’ and language translation.
• Computer vision
Computer vision uses machine learningtechniques to extract information from digital
images.10 Applications include classifying images
and detecting objects within an image.

4.
5.

Bradwell, P. The Edgeless University. Demos, 2010.
Hall, W., Pesenti, J. (2017), Growing the artificial intelligence industry in the UK. Available at https://assets.publishing.service.gov.uk/government/uploads/system/
uploads/attachment_data/file/652097/Growing_the_artificial_intelligence_industry_in_the_UK.pdf [accessed 15 July 2020]
6. Hall and Pesenti. Growing the artificial intelligence industry.
7. The Royal Society. Machine learning: the power and promise of computers that learn by example. Available athttps://royalsociety.org/-/media/policy/projects/machine-learning/publications/machine-learning-report.pdf [accessed 15 July 2020]
8. Defence Science and Technology Laboratory. The Dstl Biscuit Book. 2019 Available athttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/
attachment_data/file/850129/The_Dstl_Biscuit_Book_WEB.pdf [accessed 15 July 2020]
9. Engineering and Physical Sciences Research Council. Natural language processing. Available at https://epsrc.ukri.org/research/ourportfolio/researchareas/
natlangproc/ [accessed 15 July 2020]
10. IBM, Computer Vision. Available at https://www.ibm.com/topics/computer-vision [accessed 15 July 2020]

9

Whilst this report is focused on AI, we recognise theextent to which data science – the use of statistics,
mathematics and computer science to extract
insights and understanding from data – overlaps
with AI and machine learning.11

change in fifteen to twenty years. Beyond thistimescale, there are so many critical uncertainties
and potential unknowns that we cannot
meaningfully envisage the future of researchwithout slipping into speculative science fiction.13

Finally, throughout this report we make frequent
reference to early career researchers. Whilst there
is no commonly agreed definition of an early
career researcher, we are following the Research
Excellence Framework and others in defining thisgroup as anyone that is in their first 4-5 years of afull-time contract undertaking research or researchand teaching, e.g. a postdoc or junior lecturer.12

Finally, our primary research was completed in2019 before the emergence of Covid-19. As aresult, the bulk of this report does not directlydiscuss or address the pandemic. However, given
the long-term focus of this report, and the extent
to which the trends identified will be at play postCovid-19, its findings and recommendations arestill highly relevant to a world responding to thepandemic. Indeed, the crisis has only highlighted
the importance of the UK’s research sector, forexample, through several UK-based attempts todevelop a vaccine, and the need to ensure it retains
this position.

SCOPE
This report is focused primarily on how AI ischanging research. It seeks to address thefollowing primary research questions:• How is AI changing the UK’s research sector
and what are the barriers to its further adoption?
In particular:
How is AI changing academic researchmethods in the UK?
How is AI changing the archetypal academic
research process and research administrationin the UK?
How is AI changing the wider academic
research ecosystem in the UK?
• How will AI impact the UK research sector in
the future?
• How can policy makers, universities andbusinesses ensure that the rise of AI in the
research sector benefits the UK research sector
and wider society as much as possible?
We focus on AI for two primary reasons. First, AI
stands out from other fourth industrial revolution
technologies given the extent to which they
are widely considered to be general purpose
technologies: applied across many sectors, canenable other technologies and which are rapidly
improving. Second, for reasons of scope: it would
be almost impossible in a report of this length
to cover in appropriate detail the full breadth oftechnologies associated with the fourth industrial
revolution.
The scope of this report is further limited intimescale. It is concerned with how research will

METHODOLOGY
The content of this report draws on:• A comprehensive review of the relevant
academic and industry literature conducted for
our interim report.14• A series of semi-structured interviews withacademics working at research-intensive UK
universities. Though we have anonymised thecontributions of interviewees to this report, wehave provided below details of their seniority,
discipline and region.
Fellow, Science and Technology
Studies, London
Fellow, Neuroscience, London
Professor, Humanities, London
Professor, Social Sciences, South West
Professor, Geography, Yorkshire& Humberside
Professor, Chemistry, South East
Professor, Humanities, Scotland
Professor, Computer Science, North East
Professor, Humanities, London
Reader, Engineering, Wales
Reader, Social Sciences, Midlands

11. Defence Science and Technology Laboratory.The Dstl Biscuit Book. 2019 Available athttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/
attachment_data/file/850129/The_Dstl_Biscuit_Book_WEB.pdf [accessed 15 July 2020]
12. De montfort University. Early Career Researchers. Available at https://www.dmu.ac.uk/research/research-support/early-career-researchers/early-career-researchers.aspx
13. Which certainly has its place in conceiving what the future holds, but is not so effective at generating action-relevant policy recommendations.
14. Jones, E., Kalantery, N., Glover, B. Research 4.0 - Interim Report. Demos, 2019. Available at https://demos.co.uk/wp-content/uploads/2019/10/Jisc-OCT-2019-2.pdf[accessed 15 July 2020]10

As we hope is clear from the list above, we
engaged with researchers across a range of
disciplines and regions to get as full a picture as
possible of the use of AI in UK academic research
today. Furthermore, whilst our interviews wereprimarily with academic researchers, we also
conducted the following interviews for abroader perspective:
Former Managing Director,
academic publisher
Director, research council• A forecasting forum with external attendees fromacademia and civil society.
• A scenario planning exercise involving an internal
workshop with Demos colleagues.
• A workshop at the University of Salford.
We acknowledge that time constraints have limited
the evidence base for this report. That said, we
believe we have identified a number of key issues inrelation to AI and UK research.

11

CHAPTER 1
THE FRONTIER OF
AI-ENABLED RESEARCH
Our interim report explored how researchers are
adopting a range of AI tools and techniques,
including machine learning, natural language
processing and computer vision. This chapter
examines in further detail how AI is changing
research methods, the research process and thewider research ecosystem, with a focus on UK
universities. It is informed by the evidence review
conducted for the interim report, a series of indepth interviews with eleven leading UK academic
researchers using AI, a forecasting forum withexternal attendees from civil society and academia
and a workshop at the University of Salford. Moredetails about our methodology can be found in the
introduction of the report.
RESEARCH METHODS
What’s happening now?
As our interim report explored in detail, researchers
across the world are increasingly utilising AI
tools and techniques to support their research.
Similarly, throughout our interviews we heard of theextraordinary range and diversity of approaches
in applying AI technologies to research in UK
universities. This was visible across a wide range offields and disciplines, from STEM subjects to social
sciences, arts to humanities.
Getting more from existing data sources
We heard how AI tools and techniques are allowing
research to be carried out at a scale that would
have previously been impossible. Interviewees
described how machine learning tools have beenused by humanities researchers to ‘read’ hundredsof thousands of historic letters, thousands ofnewspaper articles. These tasks which would havetaken humans decades and would therefore becompletely unviable without machine reading.
Computer vision can also be used to analyse oldtexts that a human would be unable to read nomatter how much time they had.
New data sources
The increasing proliferation of Internet of Things(IoT) sensors is allowing researchers to undertake
analysis in close to real time, where previously thiswould have required manual data collection andpost-hoc analysis. For example, monitoring thecondition of physical infrastructure, such as bridges,
would previously have had to rely on despatchingengineers to measure key parameters. Now,
structures can be monitored in real time, allowing
instant updates of maintenance plans.
Similarly, user generated content (UGC), such associal media posts, provides the opportunity for
researchers to measure public opinion in real-time,
reducing (if not eliminating) dependence on costly
and time-consuming surveys.
New ways of gathering data are also increasing theaccuracy of existing research methods. Research
has shown that one of the main causes of drug
trial failure is the inability to monitor patients
effectively.15 In order to be monitored, patientsare required to keep a record of their medication
intake and bodily responses themselves. This islaborious and can often result in patients droppingout of a trial. To address this, wearable sensors andvideo technology can be used along with machinelearning to record and analyse patient data during
trials. For example, one interviewee described
how body-worn sensors, which patients can wear
for weeks or months, can allow us to much better
understand the effect of a treatment on people’s
lives than hospital-based testing.

15. Harrer, S., Shah, P., Antony, B., & Hu, J. Artificial intelligence for clinical trial design. Trends in pharmacological sciences, 2019, 40(8), 577-591

12

Issues relating to the ‘data explosion’Whilst the data explosion brings several important
benefits, including the potential for better quality
research and new research domains, interviewees
described how it raises a number of important
methodological and ethical challenges.
First, the wide range of new data sources has
increased the amount of time required for cleaningand preparing data for analysis. Interviewees felt
there was some scope for automation to reduce
the time this typically takes, but that this would
likely be limited and a fairly high degree of human
involvement would always be necessary. As a result,
this was placing a heavy burden on researchers’
time and detracting from the potential for these
new technologies to be labour-saving devices.
However, it is important to note that the use ofmachine learning to automate data cleaning is anactive area of current research, so this may not stillbe the case in the future.16
Second, the vast amount of data gives rise to new
ethical challenges. Researchers are responsible

for ensuring that individuals cannot be identified
in any data they publish and that their privacy isprotected. Using new sources of personal datafor research raises new questions around consent:
those who have created the data are unlikely tohave given explicit consent for their data to be
used for research purposes. Furthermore, thelikelihood of the increased linking of datasets may
make it impossible to guarantee anonymity to data
subjects.17 Models trained on historical data may
inadvertently introduce bias if they are subsequently
used in decision-making tasks, as studies of the useof AI by parole boards and in the online placementof job adverts have revealed. Researchers need to
be aware of these and other undesirable impacts
when considering whether the use of AI is justified
ethically.
A broader issue concerns the fact that potentiallyvaluable datasets collected by private companies,
such as social media platforms, are often
unavailable to researchers. This creates a very real
barrier to research and we will consider how toaddress this later in the report.

CASE STUDY 1:
ROBOT SCIENTIST ‘EVE’
In 2015 researchers at the University of
Aberystwyth and the University of Cambridge
developed ‘Eve’, an artificially-intelligent‘robot scientist’ that aims to speed up the
drug discovery process and to help make thediscovery of new drugs more cost-effective.
Eve’s primary purpose is to automate earlystage drug design. Eve’s robotic system is able
to screen over 10,000 compounds per day, but
mass screening - whilst fairly simple to automate- is still a relatively slow process, as an extremely
large number of compounds must be tested. In
addition, it is an unintelligent process, as mass
screening in this manner does not make use ofwhat is learnt during screening.
As an improvement on this process, Eve uses
machine learning and statistics to predict new

18

structures that could score better
against the tests. As Professor Ross King
describes, “bringing in machine learning to
make this process intelligent – rather than justa ‘brute force’ approach – could greatly speed
up scientific progress and potentially reap
huge rewards.” 19
Eve showed that a compound often used incancer drugs inhibits a key molecule in malaria
parasites. Professor King describes how“despite extensive efforts, no one has beenable to find a new antimalarial that targets
DHFR and is able to pass clinical trials...Eve’s
discovery could be even more significant thanjust demonstrating a new approach to drug
discovery.”

16. Krishnan, S., & Wu, E. Alphaclean: Automatic generation of data cleaning pipelines. arXiv preprint arXiv:1904.11827, 2019.
17. Lane, J. et al. (Eds.) Privacy, Big Data and the Public Good. CUP, 2014.
18. University of Cambridge, Artificially-intelligent Robot Scientist ‘Eve’ could boost search for new drugs. 2015. Available at https://www.cam.ac.uk/research/news/
artificially-intelligent-robot-scientist-eve-could-boost-search-for-new-drugs [accessed 15 July 2020]
19. University of Cambridge. ‘Eve’ could boost search for new drugs.

13AnalysisOnce the data has been extracted from the
documents, AI technologies such as machinelearning can help identify trends and classify drugs
more effectively. This combination of vast quantitiesof new data and powerful new analytical tools
is driving breakthroughs that would have been
unimaginable a generation ago. For example, AI
tools are used to discover novel materials thatmeet particular requirements, with their properties
screened in robotic labs. The possibilities for new
ground-breaking discoveries are significant.
These are just a few select examples from ourinterviewees of how AI tools and techniques arebeing used in research at UK universities. Moredetails about the specific use of these tools in UK
universities are provided in case studies throughout
this chapter.
Will the use of AI free up more time for theorising?
It is useful to consider how AI might impact
researchers’ time. Its use to automate more manualtasks could enable researchers to devote moretime to intellectually demanding and interesting
activities.
However, in general, our interviewees didn’t think
this was happening today or likely to happen
in the near future. This is because the use of AI
is frequently perceived to be extremely timeintensive, due to the amount of preparation andcleaning time that data often requires before it canbe analysed. One interviewee described how theyspent a year cleaning 160,000 letters to preparethem for analysis. This was not automatablebecause a machine learning tool wouldn’t have had
the nuance required to properly analyse the text.

And though it was possible to train early career
researchers to carry out this work, it would likely
have to be checked for validity quite extensively
by more senior researchers, viewed as a significant
drain on their time.
Furthermore, the wider research process has seen
little automation, as we will explore in greater
detail in the next section of this chapter. Thereremain many labour-intensive, manual stages to theresearch process, for example, attaching sensors,
downloading files, etc. This is partly because, in theview of one interviewee, “there’s not a Microsoft
research tool for doing all of these things”, leaving
people to join lots of different processes together.
However, some interviewees did feel that “some ofthe drudgery” had been removed by automation.
Simulations meant that those working in a lab could
focus on more “higher level activities”. They wereable to run many more experiments and the costs
associated with re-running experiments reduced.
This meant it is possible to have more confidence inresults in a shorter time frame.
In addition, it is worth highlighting that many
interviewees argued that the ideal model for theuse and deployment of AI is one in which machines
and people work closely together. This is because,
as one interviewee described, AI is at its mostpowerful when it interacts with and complements
people, instead of entirely replacing them. Tasks
or problems that require creative thinking andare not well defined from the outset will benefit
from people and machines working closely anditeratively together; the idea that the researcher can
set the parameters for a machine to then ‘go off
and do its thing’ is wrong.

14CASE STUDY 2:
LIVING WITH MACHINES
Living with machines is a research project
seeking to take a fresh look at the history of
the Industrial Revolution using data-driven
approaches. Housed at the Alan Turing Instituteand the British Library, it brings together data
scientists, historians, computational linguists
and curators from a number of different
universities.
This multi-disciplinary team aims to devise new
methods in AI that can be applied to historicalresources. This will allow digitised collections
to be analysed at scale for the first time. These
will initially be drawn from millions of pages ofnewspaper collections from the British Library’s
National Newspaper Archive and from otherdigitised collections, such as the census andgovernment collected data. The new researchmethods developed will allow researchers to

RESEARCH PROCESSIn this section we consider how AI is changing the
research process, with a focus on UK universities.
We consider the research process to be everythingthat researchers do that is not primary research
activity, from sourcing funding to reviewing
literature.
Literature Review
What’s happening now?
Reviewing the existing academic evidence is an
essential stage of the research process. AI has thepotential to significantly speed up and improve thisprocess. Researchers at MIT have demonstrated
that natural language processing techniques can be
usefully applied to the summarisation of scientific20

track change in society and culture during theIndustrial Revolution.
The Alan Turing Institute describes how the
project will be driven by a “strong collaborative
research philosophy that will be methodical,
self-reflexive and designed to evolve”. Thismeans that the development of methods, tools
and infrastructure for the project will be driven
by the central datasets used and the researchquestions. In turn, the findings from thesemethods will allow for research questions to befurther honed and nuanced. As well as being
iterative the project will also be collaborative,
with engagement with a wider audiencethroughout the project.
‘Living with machines’ is a five-year research project
funded through UKRI’s Strategic Priorities Fund. 21

papers, producing short plain-English summaries
that highlight key information.22 However, it isimportant to flag that this technology is still at an
early stage of development and is not yet mature.
This can be used by researchers to more easily andmore quickly parse the contents of a large numberof papers, reducing the amount of time required
to identify valuable sources for the literature
review itself. The use and further developmentof tools such as this could significantly speed up
the literature or evidence review stage of research,which at present can be highly time and resource
intensive. Other platforms are utilising machinelearning to uncover similar, relevant papers acrossfields and enhance searching capabilities in
specific domains.

20. The Alan turing Institute. Living with Machines. Available at https://www.turing.ac.uk/research/research-projects/living-machines [accessed 15 July 2020]
21. The Alan turing Institute. Living with Machines
22. Dangovski, R., Shen, M., Byrd, D., Jing, L., Nakov, P., & Soljacic, M. Improving Neural Abstractive Summarization Using Transfer Learning and Factuality-Based
Evaluation: Towards Automating Science Journalism.. 201915

CASE STUDY 3:
THE EXISTENTIAL RISKRESEARCH ASSESSMENT
The University of Cambridge’s Centre for
the Study of Existential Risk has developedthe ‘Existential Risk Research Assessment’to improve the evidence review stage of theresearch process.23 It utilises expert humanjudgement to classify the relevance of papers
to existential risk and then uses that dataset totrain a machine learning model that can identify
other potentially relevant papers from existing

databases of research papers. This automaticallygenerates a continually updating bibliographyof publications relating to existential risk. This
allows researchers to access “a vast amountof collective work and knowledge, rather
than having to ‘reinvent the wheel’ by doing
their own search” in a field not traditionallycategorised by publishers. 24

CASE STUDY 4:
arXlive
Nesta have launched an open-source platform,‘arXlive’, for live monitoring of research papers
on the research repository, ArXiv, in order tofacilitate innovation policy research. This tool
helps researchers follow the latest research incomputer science when “traditional meansof monitoring industrial and academic
activity are relatively slow” and research inmachine learning and related techniques
are moving quickly. 25

23. Shackelford, G., Kemp, L., Rhodes, C., Sundaram, L., ÓhÉigeartaigh, S., Beard, S., Belfield, H., et al. Accumulating evidence using crowdsourcing and machinelearning: A living bibliography about existential risk and global catastrophic risk. Futures, 116, 2020. https://doi.org/10.1016/j.futures.2019.102508
24. Shackelford et al. Accumilating evidence using crowdsourcing and machine learning.
25. https://arxlive.org/ (accessed 15 July 2020)16

Discussion

Grant Writing and Grant Awarding

Our interviewees acknowledged the potential
for AI to improve and speed up the literature
review stage. In particular, it was felt that digital
tools could point researchers towards the most
relevant information and help summarise high-level
evidence for the reader. In this way it would act as
an advanced filtering mechanism, in conjunction
with, or embedded within, a search engine. Therewas broad recognition that some of the tasks
undertaken by a human are quite repetitive, time
consuming and could be usefully automated:

What is happening now?

“The way you do a literature review as a humanis to scan the index and the introduction or
the abstract to see whether there’s something
relevant to your research there and being able
to automate that to at least filter out stuff thatyou’re not going to have to bother reading
would be very useful indeed.”
However, there was disagreement over whether it
would be possible to ‘outsource’ this stage of theresearch process to machines altogether. Someinterviewees were concerned that a summaryof an article alone would give the researcher aninsufficient level of understanding. Instead, it
was suggested that AI tools should be used tohighlight what not to read, as opposed to providing
complete summaries of relevant papers:“I think an automatically generated summary ofa research article, for most people, I think is notenough. It’s a cue to go and look further rather
than, ‘Okay, I know what’s in that I don’t need toread it.”
Alongside making research processes more
efficient, there was a view amongst someinterviewees that AI tools could help tackle researchbiases, in particular, gender bias. There was arecognition that tools could be used to analyse thereferences in research papers, for example, allowing
the proportion of citations to female authors tobe identified. This could then help researchers
to better understand the biases of their source
material and to identify fewer biased sources.
“I think maybe you could evaluate manuscripts
to see that they were referencing, you know, [...]
all the appropriate female authors working in anarea. So, in other words, that we could use it tocounter any bias in citation.”

The use of AI could significantly reduce the amountof time that researchers spend applying for research
grants and funding. At present these tasks canbe time-consuming with significant administrative
burdens, taking researchers away from conducting
research.
It could also make the evaluation of applications
for funding easier and simpler, boosting theefficiency of standard processes for research
funders. For example, software has been developed
to automatically filter out applications that fail tocomplete certain essential criteria or are unfinished.
This can leave more time for tasks that are harder toautomate, such as the qualitative review of bids.26
Discussion
There was a strong recognition amongst
interviewees that the current process of applying
for funding is extremely time consuming andreduces the time that researchers are able to spend
researching. There was some recognition that AI
may be able to address this.
However, some interviewees believed technology
could only ever play a limited role in this process;for example, automatically filling in simple
pieces of information on application forms (e.g.,administrative information, biographies etc.). Thiswas because human creativity was viewed by someas essential when writing a research proposal.
However, other interviewees felt an AI tool could
produce a first draft from which a researcher could
then work.
There were also concerns regarding the use of
AI in the review of funding applications. This
primarily related to whether the use of automatedreviewing processes would potentially screen out
novel or innovative applications, which might beseen as anomalies by an algorithmic approach toreviewing applications. It was felt that a humanwould be better than a machine at distinguishinga genuinely innovative, novel application from apoor application.
However, it is also important to flag that if
the increased use of AI tools leads to more
applications, and the amount of funding doesn’t

26. Keriann Strickland. 6 Easy Ways to Automate Your Grants Review Process. 2018. Retrieved 23 September 2019, from Submittable Blog | Submission Management
Software website: https://blog.submittable.com/easy-ways-toautomate-your-grants-review-process/17

increase to match the increased number ofapplications, we could see a situation in which
there are simply more rejections. Furthermore, if
reviewing remains the responsibility of humans,
the bottleneck that already exists in the system –reviewers of bids having too many bids to review –could just be intensified.
Peer Review
What’s happening now?
Peer review within academic research can beincredibly time consuming. Again, it is worth
considering whether AI could make this process
more efficient.
AI tools could automatically review data standards
and other methodologically laborious elementsof the review process, freeing up time for othermore qualitative tasks that humans might be better
suited to. For example, Elsevier uses the AI system
StatReviewer which checks that statistics andmethods in manuscripts are sound.27 Technology
could also save time by ensuring the expertise of
researchers is best matched with particular papers:
another time-consuming task for those involved in
the peer review process.
Discussion
Some interviewees were sceptical of whether thepeer review process could ever be conductedentirely or almost entirely by machines. This was
because it was argued that the technology is yet
to evolve to respond in relation to the lens of
nuance, context, complexity and underpinningscholarship through which research outputs appear.
One respondent argued that because “the paper iswritten for human consumption it needs to be readby a human”.
Furthermore, some interviewees argued that AI
would be unable to help with the most pressing
issues affecting peer review. For example,
interviewees reported that it was common forreviewers to take six months to review a paper and– unless you automated this process entirely, which
was not deemed possible or desirable – technology
would not help with this problem.
It seems unlikely that AI would ever entirely replace
people in the peer review process, for reasons
outlined above. What’s more, just as academics

today would be unlikely to accept the judgementof a single reviewer, we would not expect them toaccept the judgement of a single software tool.
It is therefore best to consider AI as providing
assistance to the existing human reviewers, rather
than replacing them altogether.
Indeed, interviewees recognised that there are
a number of specific parts of the peer review
process that could be improved by AI, even if themost pressing issues with peer review are social
problems without technological fixes. For example,
we heard that papers are often submitted forpeer review with grammatical or spelling errors,mismatching citation formats, references missing
from the bibliography, or mathematically impossible
data, e.g. percentages totalling over 100%. An
automated layer to the peer review process could
filter these papers out before a human peer
reviewer receives them, allowing the authors tocorrect mistakes and speed up the process. As one
respondent explained:“I spent a lot of time rejecting just really terribly
written papers from professional academics. And
I was shocked at the standards, at the sloppinessof it. And sometimes, you know, if there was asieve that could just say that was written reallybadly with very bad grammar I would havehappily put that through an automated sieve. It
was, quite frankly, a real waste.”
Finally, some interviewees recognised that systems
could be used to try to address biases in research.
For example, publishers could build tools thatanalyse the references of a paper to assess thegender balance of its sources, with papers thatfail to offer a sufficiently gender-balanced list ofreferences rejected.
RESEARCH ECOSYSTEM
This section considers how AI is changing theinstitutional nature and structure of universities.
It tries to identify institutional barriers that arelikely preventing further uptake of AI in academic
research.
Research Career Pathways
The use of AI in academic research could affectcareer pathways in academia. If there is scope forsimpler research tasks to be automated, and if
those tasks were traditionally undertaken by early

27. Heaven, D. AI peer reviewers unleashed to ease publishing grind. Nature, 2018 563, 609–610. https://doi.org/10.1038/d41586-018-07245-9

18career researchers, there could be less demand forthem. In turn, this could make it more difficult forpeople to begin a career in academia.
However, our interviewees did not believe thiswas happening today or was likely to happen in
the near future. This was largely due to the useof AI in research often being extremely labourintensive; interviewees described the huge amountof researcher time that is often required for cleaningand preparing data for analysis, for example.
Interviewees expressed more significant concerns
about whether the work of early career researchers
using AI is being appropriately recognised.
For example, there were specific concerns that
more junior researchers are often involved in thecleaning and preparation of data, but that thesetasks may not receive sufficient recognition (e.g.,
being credited as an author and/or being properly
acknowledged in academic papers).
We heard how some academic projects areseeking to avoid this problem by setting out clear
statements of principles or ‘lab charters’. Thesestatements clearly describe how work undertakenon the project will be recognised, helping to ensure
all receive appropriate recognition. For example,
the Colored Conventions Project at the Universityof Delaware sets out a series of principles to guideits investigation, including honouring the workof scholars through “equitable compensation,
acknowledgement, and attribution”.28
There was also a recognition among someinterviewees that the rise of new technologies
represents a significant opportunity for early career
researchers who are open to trying new methods
and are flexible. This was largely driven by theview that more experienced researchers may be
unwilling to learn new methods, but that demand
for these new approaches would only increase.
Thus, increased use of AI in academic research
could represent a real opportunity for early-career
researchers to get ahead of the curve. However,
it is important to flag that there could be acounteracting force to this. It might be the case that
senior academics are more willing to experimentwith new ideas because they are better establishedand have less to lose; previous studies have foundthat older and more senior researchers were more
likely to adopt ‘Web 2.0’ services in their work.29

University Infrastructure and Skills
Recent developments in AI have arisen in partdue to ubiquitous connectivity and development
of data-sharing infrastructure: increases in theavailability of computational power via the use ofGraphical Processing Units (GPUs) and cloud-based
services; increasing access to large-scale data
through the creation of massive, labelled data sets,
and cloud-based data storage; and the increasingly
widespread and free availability of powerfulmachine learning algorithms.30 If researchers areunable to access this critical infrastructure it will
hamper their ability to make the most of new tools
and technologies.
Some interviewees described experiencing
insufficient access to digital research infrastructure.
For example, sometimes requests for accessto high-performance computing were rejected
as they were not deemed reasonable requests.
Interviewees also raised concerns that because
new tools often require access to large amountsof computing power and high bandwidth, lesswell-resourced institutions both in the UK andinternationally may be unable to conduct cuttingedge research. This could have implications forthe rate of scientific discovery and create greater
inequalities in the research sector.
However, it is important to flag that poor accessto the right digital research infrastructure was
not a concern shared by all respondents. Several
interviewees described how their institutions are
well-equipped and are governed by a very proinvestment in technology mindset; we heard from
one interviewee that “everyone’s willing to invest in
new technology”.
Some interviewees also described how it isrelatively easy to acquire funding for purchasing
additional digital resources or infrastructureneeded. For example, we heard from someinterviewees how research councils have been
ready and willing to fund significant amounts ofcloud computing time. However, researchers in
the arts and humanities appeared to have greaterdifficulty accessing the infrastructure they needed.
This suggests that the response to this deficiency
may need to be targeted at certain fields or
subjects.

28. https://coloredconventions.org/about/principles/ [accessed 15 July 2020]
29. Procter, R., Williams, R., Stewart, J., Poschen, M., Snee, H., Voss, A., & Asgari-Targhi, M. (2010). Adoption and use of Web 2.0 in scholarly communications.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1926), 4039-4056.
30. Jones, E., Kalantery, N., Glover, B. (2019), Research 4.0 - Interim Report, Demos. Available at: https://demos.co.uk/wp-content/uploads/2019/10/Jisc-OCT-2019-2.pdf(accessed 15 July 2020)19

To summarise, access to digital research
infrastructure in UK universities appears to varysignificantly, with researchers in different universities
and departments having significantly different
experiences. This suggests that any attempt toimprove digital research infrastructure must firstbe informed by a rigorous assessment of existing
provision, so support can be targeted at where it ismost needed.
A lack of skills was recognised by almost allinterviewees as a major barrier to the take up
of fourth industrial revolution technologies inuniversities. There was a widespread recognition
that researchers often did not have the right skills to
make even limited use of these tools, often due to alack of appropriate training.
To generalise, the skills gap appears to be a greater
issue in the arts and humanities, with intervieweesfrom these disciplines often highlighting this gap
as a major barrier to progress and the utilisation of
new technologies. We heard how undergraduatesin these disciplines often stopped receiving any
STEM education at the age of sixteen, meaning
undergraduates are often without the skills needed
for fairly basic quantitative analysis. The lack ofquantitative training at universities within arts andhumanities programmes only makes this problem
worse, with undergraduates or graduates rarely
offered the ability to upskill in these fields; courses
may offer a quantitative or digital methods module
but this alone was perceived to be insufficient andwould likely only be optional.
Furthermore, interviewees described how there isrelatively little mid-career training available to thoseacademics who have missed out on developing
these skills earlier in their career. These issues
represent a very significant barrier to the adoptionof AI technologies and techniques. We will consider
how to address this in the final chapter of thereport.
It is important to note however, that there are notjust skills deficiencies in the arts and humanities.
Interviewees highlighted that in STEM subjects
there is still a need for domain understanding (i.e.,
related to particular fields) and technical ‘knowhow’.
This is because AI technologies are complex
and knowing how to apply these usefully to aparticular question requires a good level of domain
understanding and knowledge. But interviewees
described how finding people with this combinationof skills is challenging, with candidates often having

one without the other. We also heard concerns that,with respect to AI, researchers were learning too
narrow a set of skills, with a particular focus on deeplearning, whilst neglecting other techniques.
University Culture
The culture of universities will influence the rate at
which AI is adopted in academic research. Someinterviewees described how university culture isvery encouraging of innovation and the adoptionof new technology, whilst others had less positive
experiences.
Specifically, we found evidence of cultural
opposition to AI in certain disciplines, particularlyin the humanities. We also heard how it canbe difficult to get digital research published inmainstream humanities journals, particularly insubjects such as Classics, English or History. Thisis likely having a significant impact on what typesof research are carried out in universities, given
researchers are strongly incentivised to publishin high impact journals through the Research
Excellence Framework (REF). If these journals areless receptive to research using digital methods,
this may stifle the uptake of digital methods in thehumanities.
Interviewees often highlighted how successful
utilisation of AI in research is likely to requireinterdisciplinary working. To make full use of
these tools, research teams must combine
extensive subject expertise with a strong
technical understanding of the tools themselves.
Furthermore, to ensure that any moral or ethicalrisks are properly mitigated, research teams mayneed to involve ethicists, for example.
Whilst there are a range of strong examples ofinterdisciplinary research projects using AI in the UK
today, interviewees described that there are barriers
to this way of working within UK universities.
Strong academic incentives exist for researchers as
individuals to specialise and be single-disciplinary intheir own research. As one interviewee described,“you just specialise, specialise and specialise in
the current system...[this] creates a narrownessof thought.” Furthermore, some interviewees
described how there is relatively little mixing acrossdifferent disciplines, with departments being
described as “too closed”.31 Finally, there may also
be some bias against multi-author publications insome humanities subjects, further discouraging
multi-disciplinary working.

31. The Alan Turing Institute. Living with machines. Availalbe at https://www.turing.ac.uk/research/research-projects/living-machines [accessed 15 July 2020]

20

Similar barriers have been identified elsewhere,
including by Technopolis and the Science Policy
Research Unit (SPRU) at the University of Sussex.32
In a review of interdisciplinary research in theUK, they find that discipline-oriented culturesin universities “can act as barriers against wider
engagement between disciplines”.33 In particular,
they highlight how “friction and misunderstanding”
between teams can be created by different
approaches to evidence and rigour between
disciplines as well as different methodologicalrequirements.34 Since then, there have been anumber of UK initiatives designed to encourageinterdisciplinary research.35,36 In the next chapter
we consider what further steps can be taken to
overcome some of these barriers.
Universities and the Private Sector
In their recent government review of UK AI,
Hall and Pesenti describe how rising industry
demand for sophisticated AI skills has resulted inrising salaries, leading academics to move intobusinesses. The report argues this is affecting “theresilience and capacity of the academic network tocontinue blue sky research and to train talent.” 37
Similarly, many interviewees expressed significant
concern over the extent to which researchers withstrong AI skills are leaving academia for the private
sector. We heard how, across a range of subjects,
large numbers of graduate students that would
have previously been destined for an academic
career are now opting for the private sector, often
at one of a small number of ‘big tech’ companies.
This was particularly common in STEM subjects
such as computer science, though our interviewees

suggest it is increasingly common in thequantitative social sciences too. Whilst this trend
has not been seen equally across all disciplines,
one interviewee described how it was only a matterof time, particularly given technology companies’
increasing focus on ethics and privacy; topics onwhich humanities students are likely to be wellplaced to advise.
There is nothing inherently undesirable or wrong
about AI researchers moving to industry fromacademia. However, the extent to which researchers
move to industry and rarely if ever come back isa real challenge for universities. As a result, thismeans that the pipeline of talent within universities
is threatened and universities could be missingout on cutting-edge AI skills developed in theprivate sector. Interviewees believed a number offactors lay behind this ‘one way street’. As has been
widely discussed, pay in the private sector is often
significantly higher than a university, in part becauseof the extent to which a global shortage of AI skills
has pushed up salaries for relevant fields.
But interviewees were adamant that it wasn’t
just about pay. They described how researchers
sometimes prefer to be in the private sector, given
firms may be better equipped and have a moreinnovative working culture. There was also a strong
recognition amongst interviewees that moving
between sectors is far too difficult to do, in largepart because academic career progression depends
upon achieving a high number of publications,which may be unachievable in the private sector.
We will consider how to address this in the final
chapter.

32. Davé, A., Hopkins, M., Hutton, J., Krčál, A., Kolarz, P., Martin, B., ... & Stirling, A. . Landscape review of interdisciplinary research in the UK.
33. Hopkins et al. Landscape review.
34. Hopkins et al. Landscape review.
35. IDAP, REF 2021 Interdisciplinary Research Advisory Panel (2019), Review of the criteria-setting phase. Available athttps://www.ref.ac.uk/media/1112/
idap-criteria-phase-review-report.pdf [accessed 15 July 2020]
37. The Royal Society. APEX Awards. Availabe at https://royalsociety.org/grants-schemes-awards/grants/apex-awards/ [accessed 15 July 2020]21

CHAPTER 2SCENARIOS FOR THE
FUTURE OF RESEARCHThis chapter sets out five credible scenarios forthe research in the UK in 2040, as impacted by the
fourth industrial revolution and, in particular, therise of AI.
MOTIVATION
Making predictions about the future is fraught
with difficulty. The vast number of factors that are
involved in shaping the future makes accurate
prediction often impossible. The point of scenario
planning is therefore not to predict what the future
will look like, but to help us live with the uncertaintyof the future. This technique utilises the fact thatmany of the trends that will drive the future are
already visible around us today. By identifying thetrends and drivers that we know are important, we
can tell valuable stories about the future and try tomeet its uncertainty.38

One important value of scenario planning is thatit can allow a diverse range of perspectives to beaired, avoiding the risk of ‘group think’. It is also
helpful for decision makers to develop policies thatwill work in all conceivable futures; the aim is not tounderstand what the future will look like but rather
how we can prepare for all possible futures.
METHODTo develop our scenarios we used a four stage
process, adapted from longstanding Demos workon scenario planning.39
Stage One - Identifying relevant factors
We first identified the trends we thought may shape
the future of research in the UK using a PESTLE(Political, Economic, Social, Technological, Legal,
Environmental) analysis. We then combined thesetrends into groups, as shown in the table below.

EXAMPLE UNCERTAINTIES

Research 4.0
capability•••

What happens to Moore’s Law?
Can researchers continue to access new datasets driving new discoveries?
Do breakthroughs in AI continue or do we enter a third ‘AI winter’?

Infrastructure
capability••

Are investments made to ‘level up’ national digital infrastructure?
Can researchers access the cloud computing, storage and processing power they need?

Education, Skills& Immigration••

How well do our schools prepare students and researchers for the utilisation of Research 4.0 tools
and techniques?
Can our universities attract the best Research 4.0 experts from around the world?

Academic Culture•

Does academic culture hinder or help the adoption of new tools and techniques?

Public Attitudes•

How does the public respond to the widespread uptake of fourth industrial revolution tools andtechniques?
Do we see a ‘tech lash’?
Does the public grow increasingly concerned about the collection of confidential data and the
purposes it might be used for?

••
Governence andAnti-Trust•

How does the government respond to the growing economic, political and social power of large
technology companies?

38. Edwards, C. Futures thinking (and how to do it…) Demos, 2008. Available at https://www.demos.co.uk/files/File/PSI_planning_for_the_future_
paper.pdf [accessed 16 July 2020]
39. Edwards. Futures thinking.

22

Stage Two - Exploring driving forces
In a workshop with Demos colleagues we thenexplored how these different factors might playout. For each factor we tried to identify both thecurrent situation in relation to that factor and how
it might play out in the future. This allowed us toreach agreement on what we considered to be
key driving forces. Participants identified public
attitudes, governance and anti-trust, and education,
skills and immigration as the likely key drivers.
Stage Three - Developing and agreeing scenarios
We then created a number of scenarios, setting out
how the drivers identified in the table above could
play out and interact. We set out to develop stories
that are plausible and internally consistent.
SCENARIOS
Decentralised Research
Summary
Decentralised Research describes a world inwhich technological developments allow for a
democratisation and decentralisation of science,with independent researchers having access to thesame tools as universities and large companies. This
allows for more fluid research, with collaborations
easily springing up between the public sector,
private sector, communities and citizens. The stateplays an enabling role through less hierarchicalresearch funding and opening up access to research
for all.
DetailThe future is open. That, if anything, is the slogan
of the UK’s research sector in 2040. Research still
often takes place in the hallowed halls of ancient
universities and billion-pound labs of high-end
biotech and AI firms. But breakthroughs are just as
common in British bedrooms.
Personalised AI-enabled assistants, cheap
sensors of all kinds and vast, publicly available
datasets describing all kinds of natural and social
phenomena, from traffic to the depths of outerspace, have spurred a new generation of citizen
researchers and garden-shed inventors. Much ofthis has happened without much state direction,although public research funders were quick
to open up grants and support to community
researchers.
We have our own data vaults that securely store all
the data we collect and analyse about ourselves.
Citizens can and commonly do share that data withprivate companies or public universities to support
their research, but only with the consent of citizens,
who ultimately retain control. This does slow
research discoveries in some sectors, but forcescompanies and universities to make the public casefor the benefits of their research first.
This is not the only way the power of large
technology companies has waned. The ‘techlash’of the late 2010s extended into the 2020s, as the
public became increasingly aware of and resistantto the idea of ‘surveillance capitalism’. This inpart led to firmer competition regulation andactions to restrain the business model of thesecompanies. It has also driven people to switch tocompanies and firms that offered technology that
more aligned with their values, or even develop
their own grassroots, community driven platforms,
utilising open-source protocols. These twin factorsof tighter controls and creative destruction led toa private sector ecosystem with more and moreinteroperable players, and the same was true in theprivate sector research ecosystem.
The UK is not necessarily the top destination forinternational star talent but the pervasiveness andease of undertaking research by ordinary systems
means it has a strong backbone of competentresearchers well suited to collaborative working
and crossing disciplines. This scenario has helped
shake up academic culture too. The conservatismof the university system has somewhat given way
to a culture where titles mean less than results andindividuals are rewarded for contributing to thecollective, not just individual success.
A feedback loop has resulted with the opening upof access to journals and papers that were once
behind paywalls. Traditional academic publishers
have been disintermediated by repositories andopen-source review platforms, which allow for
more dynamic peer review through ratings andopen reviews and which reward the sharing of data,
replications and new methodologies as much ascounter-intuitive findings.

23

National Champions
Summary
National Champions describes a world where thestate works in concert with homegrown British
technology companies to push the frontiers ofscience, creating a nexus where the public andprivate are co-equal partners in research. The British
state funds and protects these companies frominternational competitors and in exchange, sharesin their successes and has a seat at the table in theirdecision-making.
DetailThe UK’s research sector in 2040 is a symbiosis
of public and private. A few home-grown
British technology companies, from hardware
manufacturers to AI developers, have been chosen
by the British state to compete in the race against
the rest of the world.
Having left the European Union and decided to
strike out alone, the UK decided that it needs towork even more closely with its private-sector if
it was going to compete in a world increasingly
dominated by large trading blocs. It did this bygoing back to a model of ‘national champions’,
exemplified by Gaullist France and followedby the UK post-war, especially in the steel andmanufacturing industries, but most commonly
associated with China today.
One notable example of creating a modern‘national champion’, was the UK working with City
investment firms to buy-out Deepmind from Google
at a not inconsiderable cost. This left Deepmind
partly privately controlled and partly state-owned.
The UK did not cut itself off from the world, if
anything it did everything it could to spread thereach of National Champions into the rest of theworld. Beyond commercial ventures, this meantassisting them in buying out foreign labs, funding
international research collaborations into basic
research their champions could exploit and offering
very favourable immigration terms to superstar
researchers and their teams to steal them away fromother countries.
The model led to increasing concentration of
investment in the already successful research
institutions in London and the South-East, withmany universities being explicitly paired with a
private company based on existing specialisationand success, e.g. the biotechnology national
champion being developed and partnered with
Cambridge University. These institutions received
all the support they could need and cooperation
was promoted even more deeply between an elitegroup of universities to match the elite group ofnational champions, who shared information andresources between them.
This only perpetuated and exacerbated existinghierarchies and inequalities within academia, even
within those universities. This model also led topoor support for areas of research perceived to
be unprofitable and reductions in outreach andpublic communications budgets, which were seen
as superfluous to increased competitiveness. It
also discouraged more well-rounded and crossdisciplinary academics in favour of commerciallyminded research managers and narrow subject
specialists who were prepared by their institutions
for industry post-masters or PhD.
Public Service Science
Summary
Public Service Science sees a world with a statedriven research sector, directed by government
missions with the government providing vastly
increased public research funding, public service
cloud computing facilities and experimenting withnew research models, e.g. in the mold of ARPA.
However, the country is more closed off to the restof the world and research is focused primarily on
serving the British nation, not others or the pursuitof knowledge.
DetailThe UK’s research sector in 2040 is driven by
a powerful central state. It pumps money into
universities and national laboratories, pushing R&D
spending up to nearly 3% of GDP; it owns all theunderlying infrastructure, including vast Britishbased cloud computing arrays fitted out with thelatest processing units, and commands the directionof research towards a series of national missions,which by the 2040s mainly focus on combatingclimate change.
In the early 2020s, the UK government faced apopulation increasingly unhappy with perceived
misuse of technology, growing regional inequalities,
a sense of declining place in the world and lack ofunity on every axis. The already weakened economy
was hit by a national crisis. The government was24forced to take the reins and take on the role of a
war-time central planner.
This was a tumultuous time and all sectors,
including research, had to be turned to deal withthe crisis, channelling resources away from blue-sky
thinking and towards immediate results. Eventually
the crisis was overcome and the UK emerged witha newly invigorated and confident central state.
Public trust in politicians and civil servants wasrestored after decades of erosion and an enlarged
public sector, not seen since the end of the Second
World War, emerged. Certain breakthroughs
in applied biomedical, energy and computing
research were seen as pivotal in ending the crisis.
Following the quasi-nationalisation of manyindustries, many private research and computational
facilities could have slipped away. However,growing concerns that more crises could be aroundthe corner and a shared sense of national trauma,along with a statist shift amongst the population
meant the UK instead stayed on a permanent war
footing, with private research capabilities either
being nationalised or remaining strongly statedirected.
All these factors led to an acceleration of previous
plans to bring research spending to 2.4% of GDP,raising it to 3% in subsequent years. The state offers
academics free access to vast amounts of public
sector computing power, on the condition they canjustify the value of their research to the state and its
security and prosperity.
The crisis had also seen the proliferation of internetenabled sensors to provide real-time data to thegovernment to manage the crisis. Alongside this,privacy and civil liberties had been rolled backand measures seen at the time as a necessary andtemporary response to the crisis, had become
entrenched. This meant the state could provide
its researchers with diverse natural and social
datasets to enable research and normalisation ofexperimentation on citizens and their data, notnecessarily with their consent.
Having felt abandoned during its time of need,
the UK turned increasingly inward, imposing
more restrictive immigration policies and reduced
collaboration with the rest of the world, focusing
instead on the need for national solidarity and selfsufficiency. The UK attracts much less international
talent, is far more secretive with the research
discoveries that it makes and has poorer knowledge

exchange with the global academic community. A
focus on self-reliance and reduction of trading links
means more British-built hardware, which is up to
the task but by no means cutting edge across theboard.
The crisis hit the South-East, London and other
wealthy urban areas most harshly. The wholecountry solidarity that came out the crisis has
meant a great rebalancing towards previously moredeprived areas of the country and an expansion ofregional specialisations, each held in similar esteem,
as a result of needing a more diverse home-growneconomy and research sector.
The UK is not a world leader in basic research but
excels in applied research focused on overcoming
national challenges, as its centralised control
and strong grip allows it to effectively channel
resources. It has also greatly enhanced its appliedresearch funding environment, developing a seriesof ARPA-like funding bodies after the success of its
original £800m experiment, each with a mission tosolve energy security, health security, cyber security
etc.
Big Tech Research
Summary
The UK’s research sector is now dominated bylarge technology companies, most of them basedoutside the UK. UK academic research, where it
continues to exist, does so in formal partnershipwith technology companies, who provide most of
the funding. Whilst the UK continues to produce
world-leading research, much of this is behindclosed doors, hampering scientific breakthroughs.
DetailOnce led by its centuries’ old universities, the UK’s
research sector in 2040 is dominated by a small
number of large technology companies. These
institutions have overtaken universities as theprimary source of cutting-edge research.
Indeed, many companies have entered into deep
and integrated relationships with longstanding
academic institutions. Courses are now offered
between companies and universities, taught byfaculty members drawn from the companies andacademia. In most of these arrangements thecompanies are the dominant partner, providing thebulk of their funding and making up the majority ofsenior decision makers. Those that graduate from25‘traditional’ universities and wish to conduct further
research will almost certainly opt to move to one ofa small number of ‘big tech’ companies.

Backwater Britain

Many of these arrangements arise as a result of
universities facing increasing budget constraints andlooking for alternative sources of funding, including
through arrangements with technology companies.
More broadly, the lack of government investment incrucial national research infrastructure creates thespace for private funding of infrastructure spendingby large technology companies with surplus profits.
This further embeds these companies in the UK’s
research ecosystem.

Backwater Britain describes a world where theUK’s research sector has stagnated and is enteringa period of long-term decline. A failure to invest
in the right technologies, infrastructure and skillsmeans that the UK has been unable to keep hold of
its position as a world-leading country for research.
The decline of the research sector affects the wider
economy, given the relationship between researchand innovation. The UK is viewed as a less attractive
place to invest and do business; its decline issymptomatic of a wider economic malaise.

The proliferation of Internet of Things devices
throughout our daily lives is complete: sensors line
our roads, smart devices are littered around ourhomes. This allows those companies with access tothis data to be able to make remarkable scientificbreakthroughs, aided by information on all mannerof issues previously unimaginable. This further
cements the shift in research dominance fromuniversities to technology companies.
In the 2020s the UK government chose not topursue tough antitrust measures against thesetechnology companies as they amassed monopoly
positions. This decision was largely driven by aperception that the UK needed to be open tobusiness and that these companies were thefuture of world-leading research. At first thismeant that technology companies were able tocontinue to attract the best researchers; later thesecompanies were then able to become serious
research institutions in their own right and rival the
traditional universities.
Whilst the UK produces much excellent research,much of it remains behind closed doors. This
scenario enables significant innovation within thecompanies that can access this research and data,
leading to further market concentration in theeconomy beyond just the research sector. However,it severely impacts the ability of those universities
not partnered with technology companies tocontinue to produce high quality research and they
suffer as a result. Serious concerns are raised that
research is not being driven by the public good butby the profit needs of technology companies. Somesay that a failure to make breakthroughs on certain
challenges, such as cancer treatment, suggests
something has gone awry.

Summary

DetailAs the UK enters the 2040s, its research sector
has been heading downward for over a decade.
British universities have plummeted down the world
rankings, with only a handful remaining in the top
100. The UK’s last Nobel Prize winner remains thephysician Peter Ratcliffe more than two decadesago. A once-flourishing commercial research sector
has largely moved overseas; only those institutions
with longstanding historical ties to the UK remain.
Research spending as a proportion of GDP falls
below 1%, far below the government’s 2.4% target.
Graduates interested in further research after theirdegree tend to move overseas, where world classresearch is now much more likely to be delivered.
The sector’s failure drives a vicious downward
spiral, with politicians unwilling to make the casefor additional funding for a failing sector. Indeed,
several universities are required to be bailed out
by the government; others that policy makers areunwilling to save collapse altogether.
Despite commitments in the early 2020s, thegovernment fails to deliver significant increases
in research funding, knocked off course by an
unforeseen global recession in the mid-2020s.
This means that universities fail to receive theresources needed to carry out increasingly costly
research. The quality of the UK’s infrastructure fails
to improve, hampering the ability of universities
to carry out world-leading research. Within
universities, researchers continued to be unable toaccess the computing power or cloud storage thatthey needed, particularly outside STEM subjects.

26

A new immigration regime focusing on home grown
talent fails to deliver. This failure results in significant
skills shortages for universities - unable to bringin both superstar researchers from overseas andthe support staff required to support an excellent
Research 4.0 agenda. Our education system also
fails to adapt to the need for the researchers of
tomorrow to be equipped with the full breadth ofskills required for a Research 4.0 career.
A conservative academic culture also hampers
progress, continuing to resist the development ofResearch 4.0 techniques in non-STEM fields. Thismeans that the UK misses out on the developmentof new fields, including significant advances in the
Digital Humanities. It also means that universities
fail to adapt their programmes of work to thedemands of a Research 4.0 agenda and too few
mid-career researchers opt to learn methods
outside their traditional domains of study.
The decline of the research sector affects the wider
economy, given the relationship between researchand innovation. The UK is viewed as a less attractive
place to invest and do business; its decline issymptomatic of a wider economic malaise.

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CHAPTER 3
A POLICY AGENDA
FOR RESEARCH 4.0
Researchers in all disciplines at UK universities are
increasingly utilising AI. This is allowing researchers
to investigate questions that would have simply
been unanswerable a decade ago. Whilst the use of
AI is increasingly widespread in academic research,steps could be taken to encourage its further takeup. This includes wider adoption of the necessary
skills, faster routes to culture change and greater
multi-disciplinary collaboration.
We presented a number of scenarios for the futureof research in the UK. The range of outcomes
make it imperative that policy makers prepare
for all possible futures, designing policies thatwill work in all conceivable futures. Furthermore,whilst the reader will have differing views on the
merits and demerits of these scenarios, Demos isdetermined to ensure that the scenario we consider
the unambiguously least desirable – ‘Backwater
Britain’ – is avoided. Again, there is a role for policy
makers to consider what steps can be taken for the
research sector to avoid this fate.
This chapter thus sets out a number of policy
recommendations that aim to ensure that:• The UK remains a world leader in research andutilises the potential of AI to help it maintain thisposition.
• The rise of AI in academic research works for thegood of wider society, including mitigating any
ethical risks associated with these technologies.
Below we set out a series of recommendations
across the following themes:• Education and Skills• Infrastructure• Research Funding• Universities and the Private Sector
EDUCATION AND SKILLS
Recommendation 1: Skills. The current post-16
curriculum should be reviewed to ensure all pupils
receive a grounding in basic digital, quantitativeand ethical skills necessary to ensure the effective
and appropriate utilisation of AI.
In Chapter One we described how students andresearchers at UK universities sometimes lack thenecessary skills to fully utilise AI technologies.
This could be holding back the ability of British
universities to continue to produce world-leading
research.
In particular, we heard that humanities and, to alesser degree, social science undergraduate andgraduate students often lack the necessary digitaland quantitative skills to utilise AI technologies andmethods. This is often due to the narrowness of
post-16 education in the UK, with many humanitiesstudents receiving no mathematical or scientificeducation past the age of 16.
Addressing this gap will require reforms to
the existing post-16 curriculum, as has been
acknowledged elsewhere. The Government’s AI
Sector Deal, for example, set out to address theshortage of STEM skills, committing £406 million
to maths, digital and technical education in schools
and the launch of T-Levels.40
But it is not just about strengthening digital andquantitative skills. The House of Lords SelectCommittee on Artificial Intelligence and theRoyal Society have both recommended that

40. HM Government. AI Sector Deal. 2019. Available athttps://www.gov.uk/government/publications/artificial-intelligence-sector-deal/ai-sector-deal[accessed 16 July 2020]28

schools should cover the wider social and ethicalimplications of AI when teaching computing and/or
machine learning.41 It is vital that pupils understandthe ethical risks of new technologies and this mustbe central to attempts to build a post-16 curriculum
fit for the age of automation.
Recommendation 2: Ethics. Universities should
ensure undergraduate and postgraduate courses inAI embed a ‘Responsible Research and Innovation’approach in the curricula to anticipate the negative
impacts of AI and designing methods to avoid or
mitigate them.
In Chapter One we described concerns that
researchers are sometimes unaware of the ethicalrisks associated with their research. This deficit
could be jeopardising the integrity of research
being carried out at UK universities and must be
urgently addressed. Currently, research projects
must provide evidence that they will conform withrules on the collection and storage of personal
data. However, there is a need for researchers toalso consider the wider ethical implications of AI
techniques. Universities have a role in meeting
this need through the provision of appropriate
training. With this in mind, the Engineering andPhysical Sciences Research Council (EPSRC) has
recently established the ORBIT project as a vehicle
for providing services to universities and industry
to promote ‘Responsible Research and Innovation’(RRI).42
Recommendation 3: Early Career Researchers.
UK research funders should require research
proposals to make a clear statement that thework early career researchers undertake will beappropriately recognised.
In Chapter One we described how increased use of
AI in academic research means that there is a rangeof new tasks associated with the preparation ofdata and that these are often undertaken by early
career researchers. It is important that this work is
appropriately recognised.
Whilst there is no commonly agreed definition ofan early career researcher, we are following the
Research Excellence Framework and others in
defining this group as anyone that is in their first
4-5 years of a full-time contract undertaking
research or research and teaching, e.g. a postdoc
or junior lecturer.43

As a first step this demands ensuring those involved
in data preparation roles are properly credited
in academic publications. We heard how someacademic projects prepare clear statements ofprinciples or ‘lab charters’ at their outset, clearly
describing how all those working on the project
receive appropriate recognition.
Research councils have a vital role to play here
in driving best practice. Requirements should be
introduced for all funding applications to include
a clear statement setting out how the work ofearly career researchers will be recognised. Given
the pressure on academic researchers to attract
funding, this is likely to be a powerful lever.
INFRASTRUCTURE
Recommendation 4: Infrastructure. A UK-wide
audit of research computing and data infrastructure
provision is conducted to consider how accessmight be levelled up.
In Chapter One we described how the quality ofdigital research infrastructure in UK universities
appears to vary significantly, with researchers in
different places, universities and even departments
having often very different experiences.
Furthermore, those in the arts and humanities
appear to have greater difficulty accessing theinfrastructure they need. This difficulty is likely
hampering the further adoption of new tools inthese disciplines, which could be holding backinnovation and breakthroughs.
The use of AI methods and tools in research isincreasing the demand for compute, data andconnectivity services: more complex AI algorithms
and growing volumes of data require morecompute power, greater data storage capacity andfast networks capable of moving large datasets
rapidly. It has also created an increased demand
for alternative ways of delivering these services.
For example, having access to scalable computeservices is often important in the model building
phase of AI projects; personal data requires asecure storage infrastructure. Some universities
may be able to satisfy these requirements, others
will find it more challenging; but even the bestequipped may find it difficult to meet the needsof collaborative projects where researchers are
distributed across different institutions.

41. The Royal Society.Machine learning: the power and promise of computers that learn by example. 2017.
Available at https://royalsociety.org/-/media/policy/projects/machine-learning/publications/machine-learning-report.pdf [accessed 15 July 2020]
42. https://www.orbit-rri.org/ [accessed 16 July 2020]
43. De montfort University. Early Career Researchers. Available at https://www.dmu.ac.uk/research/research-support/early-career-researchers/earlycareer-researchers.aspx

29

UKRI needs to ensure that researchers across the
UK can access fast, secure and reliable digital
infrastructure. A review should address the role ofnational digital research infrastructure for ‘levellingup’ access to compute and data storage, paying
particular attention to the need to provide support
throughout the regions of the UK for high-speed
connectivity, scalability, security and, collaboration
between the public and private sector. To minimisebarriers to the latter, it is essential that collaboratingorganisations be able to share compute anddata resources seamlessly, which in turn, requires
a common framework for Authentication,
Authorisation and Accounting Infrastructure (AAAI)
and high-speed connectivity throughout the regionsof the UK. Despite efforts over the past fifteen
years,44 however, progress towards AAAI has beenslow and a solution has now become critical.45
RESEARCH FUNDING
Recommendation 5: AI Incentives. UKRI should
consider that future spending on research anddevelopment incentivises institutions to utilise AI
wherever it can offer benefits to the economy andsociety.
As has been widely documented, the UK spends
less on research and development than othercomparable countries. Total public and private
spend on research and development in the UK
currently stands at roughly 1.7% of GDP, compared
to an OECD average of 2.4%. The under-spend islikely to be limiting the potential of our researchsector. We therefore strongly support the
government’s commitment to increase levels of
research and development spending to 2.4% as astep en route to 3%.
There is a need to ensure that any increased
spending is targeted where it will have the mostimpact. As we explained in Chapter One there isgreat potential for AI to transform whole researchareas. We therefore think it is right that some of thisincreased spending is targeted at research whichmakes full use of these technologies.
Recommendation 6: Interdisciplinary/Multidisciplinary Research. UKRI should
devote more funding to interdisciplinary andmultidisciplinary research programmes, such as theStrategic Priorities Fund.

We have seen throughout the report how
successful utilisation of AI in research requires
strong interdisciplinary working. Research teams
must combine extensive subject expertise with astrong technical understanding of methods andtools. Furthermore, there is a need to involve thoseoutside the sciences; to ensure that any moral or
ethical risks are properly mitigated, research teams
may wish to involve philosophers, for example.
However, there are a number of barriers tointerdisciplinary research in universities. As detailed
by a 2016 review of interdisciplinary researchin the UK, the often monodisciplinary focus offunding opportunities reduces the likelihood ofinterdisciplinary research being funded.46 UKRI’s
Strategic Priorities Fund, designed to spearhead
multi and interdisciplinary research and innovation,
goes some way to addressing this problem; this andother similar funds must continue to be expanded.
UNIVERSITIES AND THE PRIVATE SECTOR
Recommendation 7: Researcher Mobility.
Universities should take steps to ensure that it iseasier for researchers to move between academia
and industry, for example, by putting less emphasis
on publications, and recognising other outputs andmeasures of achievement when hiring for academic
posts.
In their recent government review of the UK’s AI
industry, Hall and Pesenti describe how risingindustry demand for highly AI skills has resulted
in rising salaries, leading academics to move intobusinesses. They describe how this is placing“a strain on the resilience and capacity of theacademic network to continue blue sky researchand to train talent.”47
Interviewees in Chapter One expressed concern
about talented researchers increasingly leaving
academia to work in commercial sector technology
companies. There were worries that this represents
a major risk to the future of research at UK
universities, with the pipeline of talent at risk ofdrying up. This problem has been noted widely
elsewhere. In a recent review of data science,
the Royal Society describe how “Big internetcompanies are adding to pressure on universities,
which are already struggling to retain professors
and other employees.”48

44. E.g., The European Commission. Advancing Technologies and Federating Communities: A Study on Authentication and Authorisation Platforms For
Scientific Resources in Europe. Terena, 2012 Available at https://www.terena.org/publications/files/2012-AAA-Study-report-final.pdf
45. UKRI. The UK’s research and innovation infrastructure: opportunities to grow our capability. Available at https://www.ukri.org/files/infrastructure/theuks-research-and-innovation-infrastructure-opportunities-to-grow-our-capacity-final-low-res/
46. Technopolis and University of Sussex. Landscape Review of Interdisciplinary Research in the UK. 2016. Available at
http://sro.sussex.ac.uk/id/eprint/65332/1/2016HEFCE_Landscape%20review%20of%20UK%20interdisciplinary%20research.pdf
47. Hall, W., Pesenti, J. (2017), Growing the artificial intelligence industry in the UK. Available at https://assets.publishing.service.gov.uk/government/
uploads/system/uploads/attachment_data/file/652097/Growing_the_artificial_intelligence_industry_in_the_UK.pdf [accessed 15 July 2020]
48. Royal Society. Dynamics of data science skills: How can all sectors benefit from data science talent? 2019. Available athttps://royalsociety.org/-/media/policy/projects/dynamics-of-data-science/dynamics-of-data-science-skills-report.pdf

30

Whilst it is unrealistic for universities to compete on
pay with commercial sector companies, other steps
can be taken to ensure that the talent pipeline inuniversities does not dry up. The steps include
making it easier for researchers to move between
academia and the private sector throughout theircareers. This is at present often difficult to do inpractice, in large part because academic career
progression requires attaining a high number ofpublications, something that may be unachievablein the private sector. University hiring culture shouldchange to better recognise industry experiencealongside academic experience.
Recommendation 8: AI Fellowships. UKRI
should create and fund a number of ‘AI industry
fellowships’ at UK universities.
Alongside encouraging culture change inuniversities, steps can be taken to encourage
greater movement of minds between universities
and the private sector. For example, the Royal
Society recently recommended the creation andfunding of joint positions across industry andacademia.49
Building on this direction of travel, more short-term
academic posts could be created at universities forindustry researchers. These could allow those based
in industry to spend a period of time researching
and teaching in academia. This would encourage
sharing of best practice between academia andindustry, allowing for a valuable cross-fertilisationof ideas. It could also give industry researchers theopportunity to gain the academic experience andpublications to later return to academic research
on a more permanent basis.

49. Royal Society. Dynamics of data science skills: How can all sectors benefit from data science talent? 2019.
Available at https://royalsociety.org/-/media/policy/projects/dynamics-of-data-science/dynamics-of-data-science-skills-report.pdf

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