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Artificial Intelligence, March 2018

Contents

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1. Introduction

What is artificial intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Brief History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Current state of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

In Focus: Turing Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2. AI and the Government

Current usage and government support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

In Focus: European General Data Protection Regulation. . . . . . . . . . . . . . . . . . 8

3. AI, Automation and the UK Economy

AI and the Macroeconomy: productivity and economic growth. . . . . . . . . . . 9

AI and the labour market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

AI, inequality and labour relations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

In Focus: Universal Basic Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction

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In October 2016, the House of Commons’ Science and Technology Committee released a report1 on Artificial Intelligence (AI) in the UK. The report aimed to identify ‘the potential value and capabilities [of AI and robotics], as well as examining prospective problems, and adverse consequences, that may require prevention, mitigation and governance’. This implies a paradox; the increased use of artificial intelligence (AI) could deliver major social and economic benefits but the associated ethical and legal risks emphasise the importance of policy prescriptions and institutional regulation.

What is Artificial Intelligence?

There is no single definition of AI. Defined loosely, AI refers to a computerised system exhibiting behaviour previously thought of as requiring ‘intelligence’. The Government Office for Science defines AI as ‘generally the analysis of data to model some aspect of the world. Inferences from these models are then used to predict and anticipate possible future events’2. The term ‘AI’ consequently means, as stated by the IBA Global Employment Institute, ‘investigating intelligent problem-solving behaviour and creating intelligent computer systems’3. Although the boundaries of AI are contentious and have shifted over time, the core objective of AI research and applications is to automate and simulate ‘intelligent’ behaviour. ‘Intelligent’ capabilities that can be replicated include perception, learning and adaptation; communication; optimisation of procedures and parameters; cognitive thought and planning autonomy; creativity; and extracting predictions from varied digital data.

AI is perhaps best described as an umbrella term to cover diverse techniques that have developed from varied research fields including statistics, computer science and cognitive psychology. One may recognise distinctions between specific technologies and terms (machine learning v. deep learning included), though a more useful classification comes from PWC who consider there to be four ‘AI elements’: (1) automated intelligence - automation of manual, routine tasks; (2) assisted intelligence - helping to perform tasks more efficiently; (3) augmented intelligence - helping people to make better decisions (4) autonomous intelligence - automating decision-making processes without human intervention4.

The applications of AI systems are varied, ranging from communicating with computers linguistically, deriving new insights from government data, autonomous/adaptive robotic systems, smart supply chains and video game design. Everyday examples include product recommendations by online retailers, filters for email spam that recognise junk email and the growing use of ‘virtual assistants’ (Apple’s Siri and Amazon’s Alexa being prominent examples). AI has been applied to, and has changed the business practices of the financial services, law, health, accounting, tax, audit, architecture, consulting, customer service, manufacturing and transport industries. And the list of applications is growing rapidly: experts forecast that rapid progress in the field of AI will persist - machines will continue reaching and surpassing human performance on a growing variety of tasks.

Brief History:

The term ‘AI’ was coined in 1956, though its roots can be traced to (at least) the 1940s where the development of early computer systems created the possibility of constructing intelligent machines. In 1950, the idea of AI was crystallised in ‘Computing Machinery and Intelligence’, where Alan

1 House of Commons Science and Technology Committee, Robotics and artificial intelligence, October 2016

2 Government Office for Science, Artificial intelligence: opportunities and implications for the future of decision

making, February 2016 3 IBA Global Employment Institute, Artificial Intelligence and Robotics and Their Impact on the Workplace, April

2017 4 PWC, The economic impact of artificial intelligence on the UK economy, June 2017

Introduction

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Turing5 posed the question ‘Can machines think?’ before amending the question in terms more amenable to an empirical test (the ‘Turing Test’)6.

Some of the first applications of computers were AI programs, where researchers wrote programs that could perform elementary reasoning tasks. In 1952, Arthur Samuel built a program that learnt to play checkers which eventually beat the Connecticut state checkers champion in 1961. In 1957, Newell and Simon 1957 built Logic Theorist, a program that could discover proof in propositional logic, and developed the General Problem Solver which used ‘means-ends’ analysis to solve problems, including symbolic integration and algebraic word problems.

During the 1960s, systems that understand language were developed. For example, Bobrow’s 1967 STUDENT program could solve high school algebra tasks. Wood’s LUNAR system was able to answer spoken English questions about rock samples collected on the moon by NASA. During the 1970s and 1980s, a large body of work emerged on ‘expert systems’, where the aim was to build systems capable of embodying knowledge of an ‘expert’ in a particular domain so a computer could carry out ‘expert’ tasks. For example, Feigenbaum et al.’s 1980 DENDRAL system could predict the structure of organic molecules given their chemical formula.

During the 1990s and 2000s, research progress in AI accelerated as researchers focused more on sub-categories of AI (including planning and machine learning) and applied AI to ‘real-world’ problem solving (such as image recognition and medical diagnosis). In 1997, IBM’s computer Deep Blue attained a landmark in the development of AI by defeating world chess champion Garry Kasparov by 3.5 to 2.5 in a six game match7.

Since 2010, the capacity for AI has increased considerably, driven by five factors: (1) new and larger volumes of data; (2) increased supply of experts with specific high-level skills; (3) increasingly powerful computing capacities; (4) improved machine learning approaches and algorithms and (5) rapid growth of private investment in AI8. During this period, the pace of improvement has been unprecedented. For example, in terms of speech recognition, the Google Home smart speaker’s error rate has improved from 8.5% (July, 2016) to 4.9% (May, 2017).

Current State of AI:

There are two classes of AI:

‘Narrow AI’ (applied or weak): addresses specific tasks such as playing strategic games, language translation, automated vehicles and image recognition. An impressive amount of progress has been made in this area, with narrow AI underpinning a variety of commercial services including trip planning, shopper recommendation systems, advertisement targeting, and is being applied in medical diagnosis and scientific research.

‘General AI’ (strong or full AI): computers exhibit intelligent behaviour (at least) as advanced as a human across a number of tasks, i.e. the processes in the computer are intellectual, self-learning processes whereby the computer, by virtue of software/programming, can ‘understand’ and

5 Alan Turing, photo courtesy of Wikimedia Commons

6 Turing, A. (1950), Computing Machinery and Intelligence, Mind 49,

7 Deep Blue & Gary Kasparov, photos courtesy of Wikimedia Commons

8 Executive Office of the President National Science and Technology Council Committee on Technology,

Preparing for the Future of Artificial Intelligence, October 2016

Alan Turing

Deep Blue and Gary Kasparov

Introduction

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augment their own behaviour. Experts suggest this will not be achieved for decades and attempts to reach General AI by expanding Narrow AI solutions are making little progress.

The economic use of AI can be separated into four categories:

Machine Learning (ML): algorithms that change in response to their own output or ‘computer programs that automatically improve with experience’9. At its core, ML is the process of automatically discovering patterns in data and making decisions based on such patterns. There are many variations of algorithm used in machine learning, though the key distinction between them is whether their learning is ‘supervised’ or ‘unsupervised’. Supervised learning involves using a labelled data set to train a model, which can then be used to classify an unseen set of data - useful for identifying elements in data, predicting likely outcomes or spotting anomalies. Unsupervised learning involves using an unlabelled data set and asking the AI to find structure in the data.

Deep Learning: supervised learning technique combining layers of neural networks to automatically identify features of a data set relevant to decision-making and enable the recognition of extremely complex, precise patterns in data. This is machine learning based on a set of algorithms that ‘attempt to model high-level abstractions in data’10.

Autonomy and Automation: autonomy refers to the ability of a system to operate and adapt to changing circumstances with reduced human control. Automation occurs when a machine does work that might previously have been done by a human. The potential impact of automation on employment has dominated political debates since the Industrial Revolution.

Robotics: machines that are ‘capable of carrying out a series of physical actions on behalf of humans’ and may in future increasingly use AI to perform their tasks. Robotics and autonomous systems (RAS) refers to ‘physical and software systems that can perceive their environment, control their actions, reason and adapt’11.

9 Mitchell, T. M. (2006), The Discipline of Machine Learning, July 2016

10 Kochura, Y., Stirenko, S. and Gordienk, Y., Comparative Performance Analysis of Neural Networks

Architectures on H2O Platform for Various Activation Functions, October 2017 11

House of Commons Science and Technology Committee, Robotics and artificial Intelligence, September 2016

In Focus: The Turing Test

Turing attempted to transform the question ‘can machines think?’ into a more concrete

form through the Imitation Game (IG). The game requires a man, a woman, and an

interrogator whose gender is unimportant. The goal is for the interrogator to identify

which of the participants is a man and which is a woman through the answers to the

interrogator’s questions (type-written or through an intermediary). The goal of both the

man and the woman is to convince the interrogator that he/she is the woman and the

other is not. Turing then proposed a modification of this game: one of those two

participants would be replaced by a machine and the goal of the interrogator would be

to identify which is human and which is a machine. If, under these conditions, an

interrogator is less than 50% accurate - as likely to pick human or computer - then the

computer must be a sufficient representation of a human and therefore possesses

intelligent abilities.

AI and the government

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The Prime Minister says she wants the UK to ‘lead the world’ in deciding how AI can be deployed in a ‘safe and ethical manner’ and that the UK should be recognised as the first in its preparedness to ‘bring artificial intelligence into government’12.

Current usage and government support

The government is already using data science techniques such as machine learning and their usage is growing through the work of the Government Data Programme. For example, HMRC has utilised AI and machine learning with contract handling, casework decision making and helping customers through ‘effective self-service to positive effect’13. Likewise, the Pensions Regulator has collaborated with the Better Use of Data Team at the Government Digital Service to better use data on pension schemes through Machine Learning.

A report commissioned by the government emphasised the potential for AI to make existing services (health, social care, emergency services) more efficient by anticipating demand, tailoring services, enabling resources to be deployed to greatest effect, making it easier for officials to use more data to inform decisions and making decisions more transparent14.

Nesta provides an example of practical applications of AI by highlighting the use of machine learning (or ‘big data’ and ‘big analytics’) to support social care services in the Newcastle’s Family Insights Programme, which pairs evidence-based social work practice with arduous data analysis to give insight to social workers when deciding how best to support families. In terms of prediction and optimisation, they highlighted London Councils’ sharing of data to crack down on rogue landlords through a pilot for a London Office of Data Analytics. In terms of efficiency, they highlight Camden council’s integration of data from different teams’ IT systems through the use of a Residents Index which ‘stripped out inefficiencies from back office processes, saving money and improving services for citizens’15. Finally, Southend-on-Sea borough council became the first local authority to use a robot in its services when they purchased ‘Pepper’ from Japanese company Softbanks. Pepper will work as a social care employee, involved in community engagement, awareness raising and reminiscence activities16.

The work of the Data Science Partnership, collaboration between the Government Digital Service (GDS), Office for National Statistics (ONS) and the Government Office for Science, is raising awareness of the potential of data science across government and increasing the data capabilities through delivering ‘mentored projects’17. There is also a new UK advisory body, the Centre for Data Ethics and Innovation, which will work with regulators, industry and government to pave the way for ‘sensible’ AI adoption, announced in the Autumn Budget 2017.

The government has announced a number of investments in AI-related developments. The UK Digital Strategy moved money into AI research in two areas - £17.3 million in Engineering and Physical

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BBC News, UK PM seeks 'safe and ethical' artificial intelligence, January 2018 13

HMRC, Artificial intelligence and machine learning; exploring the possibilities, July 2017 14

Government Office for Science, Artificial intelligence: opportunities and implications for the future of decision making, February 2016 15

Nesta, Using data in government and public services A practice guide, May 2017 16

Photo courtesy of Wikimedia commons 17

Cabinet Office, Building capability and community through the Government Data Science Partnership, July 2017

Pepper the robot

AI and the government

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Sciences Research Council (EPSRC) grants to support the development of new Robotics and Artificial Intelligence technologies in UK Universities and £6.5 million capital investment to support further collaboration within the UK Robotics and Autonomous Systems Network. In 2015, the EPSRC created the Alan Turing Institute, the national institute for data science. The Autumn 2017 Budget also announced funding of £75 million to ‘take forward key recommendations of the independent review of AI, including exploratory work to facilitate data access through “data trusts”’18.

The government has also declared £30 million to test the use of AI and EdTech in online digital skills courses, £20 million capital funding for the establishment of the Institute for Coding to develop specialist graduate level technical IT skills and £21 million investment in Tech Nation which will expand the reach of Tech City UK. The ‘Industrial Strategy Challenge Fund’ will allow £93 million over the next four years for AI-related research specifically focused on making public services and industry more productive and efficient.

Challenges:

The Prime Minister aims for Britain to ‘lead the world’ in AI-related research, though this ambition seems farfetched when considering the level of UK investment compared to other states. The government’s allocation of £75m in the 2017 Autumn Budget for developing AI constitutes a mere 4% of the EU’s £1.9 billion planned spend. Director-General of the European Commission, Robert Viola, identifies the potential for AI to allow the European Union to maintain a ‘strong leadership role through shaping the [artificial intelligence] debate and future debates’19, which raises questions about the UK’s soft power post-Brexit. The disparity becomes clearer when considering China’s £110 billion national plans for AI - Beijing alone is spending £1.6 billion to build an AI ‘development park’ and Tianjin has earmarked a £3.4 billion fund to further AI research.

There is a lack of an ‘implementation plan’ to introduce AI in government. This is borne out by various organisations in their response to the ‘Government Transformation Strategy’ published in February 2017, which set out a course for digital government and improving public services. The Institute for Government (IfG) identifies the lack of ‘specifics on how progress will be made’, the lack of priority given to public services, including those pivotal to managing Brexit, and the effects this will have on accountability as barriers to the development of AI20. The IfG suggests introducing an AI implementation minister to bring focus and accountability to AI policy - in October, the United Arab Emirates became the first nation with a government minister dedicated to AI.

Current legislative frameworks are not adapting fast enough. Reform Think Tank highlights that data protection laws in the UK favour minimising the amount of data can be collected and limiting what can be done with it. This is exemplified by the 1998 UK Data Protection Act and 2016 EU General Data Protection Regulation21, which will come into effect in May 201822. Both govern the use of citizens’ data by government analysts, protecting privacy rights and safeguarding personal identity. This runs contrary to the basic principles underpinning machine learning algorithms, which require free-flowing data to draw insights, suggesting that advanced AI may require a trade-off in terms of data protection.

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HM Treasury, Autumn Budget 2017, November 2017 19

Viola, R., The future of robotics and artificial intelligence in Europe, February 2017 20

IfG, Improving the management of digital government, June 2017 21

Photo courtesy of Pixabay 22

Harwich, E., AI could transform the way governments deliver public services, Guardian, February 2017

AI and the government

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In addition, the productive use of AI in government depends on resolving wider AI-related issues, including access to skills. The development of AI will require long-term development of high-level STEM skills. As DeepMind’s evidence to the House of Commons Science and Technology Select Committee Inquiry into Robotics and Artificial Intelligence stated, ‘one of the most important steps we must take is that current and future workforces are sufficiently skilled and well-versed in digital skills and technologies, particularly STEM subjects’23. However, the significant STEM skills gap across the UK, driven by weak basic skills and underrepresentation of women and minority groups, has led to an acute talent shortage.

Cath et al. (2017) highlight that, while closing the STEM skills gap remains important, it ‘should be seen as more than an aim in itself but also as an opportunity for the government to develop an explicit vision of the role of AI in society’24. This links to questions regarding which functions of government should be automated or made ‘smart’ and to what degree. Deloite identify four possible solutions:

(1) Relieve: AI takes over mundane tasks, freeing workers for more valuable work.

(2) Split up: automating as many jobs as possible, leaving workers to do the remainder and possibly supervise the automated work.

(3) Replace: technology is used to do an entire job once performed by a worker.

(4) Augment: technology makes workers more effective by complementing their skills25.

23

House of Commons Science and Technology Committee, Robotics and artificial Intelligence, September 2016 24

Cath, C., Wachter, S., Mittelstadt, B. et al., Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach, March 2017 25

Deloitte, AI-augmented government: Using cognitive technologies to redesign public sector work, April 2017

In Focus: European General Data Protection Regulation (GDPR)

The European GDPR is an EU regulation intended to harmonise data privacy laws,

strengthen and unify data protection for all individuals within Europe and reshape the way

organisations across the region approach data privacy. The regulation comes into effect on

May 25th, 2018 and will supersede the 1998 Data Protection Act, enacted following the

1995 EU Data Protection Directive. The GDPR requires data controllers to demonstrate

compliance with a raft of new responsibilities including carrying out a data protection

impact assessment for each ‘risky process/product and to implement data protection by

design and by default’. This creates an obligation for AI parties to integrate the data

governance process with appropriate safeguards, including data minimisation and data

portability. Informed consent is another key principle for the GDPR and this could be

problematic for the operation of AI. Lastly, the GDPR provides that individuals shall have

the right ‘not to be subject to a decision based solely on automated processing’ unless that

decision is provided by law or is based on the subject’s consent. This will require to

increasing transparency surrounding AI usage. However, it will be difficult to explain some

AI decisions when they are based on vast data combinations.

AI, Automation, and the Economy

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Debates surrounding the economic impact of AI on the UK economy are contentious, with most discussions centring on the potential for AI to engender profound structural changes to the UK labour market. Despite the range of predictions, the emergence of machine learning, advances in robotics, ‘big data’ and autonomous systems (ushering in a ‘new age of automation’26) is likely to have significant implications for the economy, labour markets, socioeconomic disparities, skills and capital-labour relations.

AI and the Macroeconomy: productivity and economic growth:

There is evidence to suggest that AI development could drive productivity growth and therefore boost economic growth, though this is dependent on the extent to which these technologies mirror the impact of past forms of technological advancement. For centuries, the UK economy has adapted to technological progression and economic progress has been driven by automation. The first Industrial Revolution used steam and electricity, followed by relays, transistors and semiconductors, to automate many production processes. However, the development of new production

processes can lead to short term job losses. Despite being the driving force for industrialisation and increased productivity, the steam engine replaced many workers once introduced in factories and led to periods of social unrest.

Made Smarter, an industry-led review headed by the CEO of Siemens UK, Professor Juergen Maier27, reported that the UK could boost manufacturing by £455 billion, increasing sector growth up to 3% per year and creating a net gain of 175,000 jobs, if the UK took full advantage of the ‘fourth industrial revolution’, based on robotics, AI and other cutting-edge technologies28. In the 2017 Industrial Strategy, the Government stated that ‘embedding AI across the UK will create thousands of good quality jobs and drive economic growth’29, paralleling the Wendy and Pesenti (2016) report for the Department for Business, Energy and Industrial strategy, which postulated that impacts will be ‘positive, large, and widely spread across sectors, with uneven rates of uptake’. The reports also predict that there will be significant gains across all UK regions30.

Research by Accenture (2016)31 and Chen et al. (2016) has investigated the potential impact of AI on the economy. The former estimates that AI could add £630 billion to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%32. The latter postulates that the economic effects of AI will include ‘both direct GDP growth from sectors that develop or manufacture AI technology’ and ‘indirect GDP growth through increased productivity in existing sectors that employ some form of AI’. The research expects productivity improvements in existing sectors through more efficient processes, decision making and increased knowledge and access to information33. PWC (2017) estimates that UK GDP will be up to 10.3% higher in 2030 as a result of AI (£232bn), driven by productivity gains (1.9%) and new firm entry stimulating demand (8.4%)34.

26

Manyika et al., 2017, A future that works: automation, employment and productivity, January 2017 27

Juergen Maier, photo courtesy of Flickr 28

Department for Business, Energy & Industrial Strategy, Made Smarter Review, October 2017 29

HM Government, Industrial Strategy: Building a Britain fit for the future, November 2017 30

Department for Business, Energy & Industrial Strategy, Growing the Artificial Intelligence Industry in the UK, October 2017 31

Photo courtesy of Business Wire 32

Accenture, Why artificial intelligence is the future of growth, September 2016 33

Tech UK, AI and the Communications Sector – Challenge or Opportunity?, September 2017 34

PWC, The economic impact of artificial intelligence on the UK economy, June 2017

Juergen Maier

AI, Automation, and the Economy

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AI offers massive gains in efficiency and performance in most/all industry sectors for a number of reasons, most evidently by helping businesses use resources more efficiently, reducing the burden of searching large sets of data and streamlining the way we interact with data. Routine administrative and operational jobs can be learned by ‘software agents’ (or ‘bots’) which can prioritise tasks, manage interactions with colleagues and plan schedules more efficiently. This can decrease the number of labour hours needed to create units of output and decrease the cost of products and labour. Examples from the field of AI include ‘smart factories’, driverless cars, delivery drones and 3D printers which produce highly complex objects without changes in the production process.

However, it is worth noting that the recent acceleration of technological advances in the UK – including that of machine intelligence and robotics – has coincided with stagnating productivity. This seems to indicate that the vision of a more productive future with increasingly limited human interaction advanced by tech evangelists is not necessarily forthcoming. However, the extent to which any economic gains are realised will be driven in large part by the rate of advancement and rate of deployment of new technology. In the former, more limited advances and applications (weak AI) will correspond to more limited economic effects and more substantial progress (strong AI) will correspond to more significant economic effects. In the latter, the speed of technological change and speed of AI’s deployment by firms across the economy will likely have a significant effect on AI’s ability to increase productivity, wage growth and economic development.

AI and the labour market:

It is widely accepted that AI will automate some jobs, but the debate surrounding whether AI, big data and autonomous systems will affect the effect labour market differently than past waves of automation is subject of debate, with little consensus on how many jobs could be lost to automation.

Frey and Osborne (2013) predict that 35% of UK jobs will be affected by automation over the next 10 to 20 years, though these calculations did not consider factors such as ease of implementation or

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Data in Accenture, Why artificial intelligence is the future of growth, September 2016, p.16

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Accenture (2016) - Artificial Intelligence and Annual Growth Rate

AI, Automation, and the Economy

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jobs created by automation36. PWC estimates that 30% of UK jobs will be affected by automation by the early 2030s, lower than the US (38%) and Germany (35%). The Bank of England’s Chief Economist estimates up to 15 million jobs will be affected by automation over the next 10 to 20 years whilst the IBA Global Employment Institute suggests that one third of jobs requiring a bachelor’s degree will be performed by machines or intelligent software37. Again, this is not net change - they do not take account of jobs created due to automation or the capacity for a stronger economy to support more jobs in existing fields, such as the leisure or creative industries.

Deloitte found that the sectors with the highest number of jobs at risk of automation were transportation and storage (74% of workforce), health and social care (28%) and wholesale/retail (59%)38. Alternatively, PWC found the sectors with the highest number of jobs at risk were transportation and storage (56% of workforce), manufacturing (46%), health and social care (17%) and wholesale/retail (44%)39. The heightened risk to jobs in transportation stems from plans to roll out AI in areas including traffic management and introduce autonomous vehicles such as driverless cars, autonomous agricultural vehicles and aircraft. The Modern Transport Bill, announced in 2016, will aim to ‘put the UK at the forefront of autonomous and driverless vehicles ownership and use’40.

In contrast, the OECD suggests that only 10% of UK jobs are at risk due to automation which compared to a range of 6% (South Korea) and 12% (Austria)41. Mokyr (2015) argues that AI development will create new products and services and that these product innovations will result in ‘unimaginable new occupations’ similar to the First Industrial Revolution42. Additionally, the tasks (and skills) that constitute particular jobs could change considerably in the future - the OECD suggests day-to-day work could change for 25% of occupations.

It is likely the increased complexity and technological intensity of the economy will lead to a continued increase in demand for high-skilled labour contra low-skilled labour. For example, Deloitte found that while technological advances have contributed to the loss of 800,000 low-skilled occupations between 2001 and 2015, it has also contributed to the creation of 3.5 million high-skilled jobs during the same period43. UKCES estimates that most jobs created in the decade 2012 to 2022 will be high-skilled and almost half of all employment is set to be in managerial, professional or associate professional roles by 2022/202444. In Britain, there is already a chronic skills shortage which will only intensify given that business expects their demand for skilled workers to increase.

There is significant evidence that STEM and digital skills will be in increasing demand, especially given the growing demand for skilled STEM workers in the digital technology workforce. ‘Soft skills’ that are broadly applicable across a range of industries and positions are also likely to increase in importance. Soft skills include communication, team working, work ethic, conflict resolution,

36

Frey, C. and Osborne, M., The Future of Employment: How Susceptible are Jobs to Computerization?, September 2013 37

House of Commons Library, Artificial Intelligence and Automation in the UK, December 2017 38

Written evidence submitted Deloitte, Robotics and artificial intelligence inquiry, House of Commons Science and Technology Committee, April 2016 39

PWC, UK Economic Outlook, March 2017 40

Handley, L. What is the modern transport bill?, Guardian, January 2017 41

OECD (2016), The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis 42

Mokyr, J. (2015), The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different, Journal of Economic Perspectives. 43

Written evidence submitted Deloitte, Robotics and artificial intelligence inquiry, House of Commons Science and Technology Committee, April 2016 44

UK Commission for Employment and Skills, The future of jobs and skills, April 2014

AI, Automation, and the Economy

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creative thinking, time management and resourcefulness. They are becoming more important as they are uniquely ‘human’ and therefore less susceptible to automation.

45

AI, inequality and labour relations:

Research consistently finds that jobs threatened by automation are highly concentrated among lower-paid and lower-skilled workers. This will place downward pressure on employer demand for this group of workers, deflating wages and increasing inequality. Deloitte has found that jobs paying less than £30,000 a year are nearly 5 times more likely to be replaced by automation than jobs paying over £100,000 and in London; such lower-paid jobs are more than 8 times more likely to be replaced. This is echoed by the Institute for Public Policy Research - whilst they dismiss the idea that AI-driven automation will lead to job losses and instead predict that workers will be reallocated into different roles, they insist that without ‘managed acceleration’, automation could exacerbate wealth inequality through the simultaneous erosion of poorer workers’ wages and boost to highly-skilled workers’ wages.

There are three possibilities. Firstly, automation may lead to continuing skills-biased technological change - AI favours workers with more skills whilst substituting those with less skills. Second, automation may lead to capital-biased technological change whereby the share of income that goes to capital increases as AI favours investment in tech. Third, automation may lead to ‘superstar-biased’ technological change whereby the benefits of technology accrue to an even smaller portion of society than just highly-skilled workers46. In all three cases, the benefits of AI to productivity and

45

Data in Deloitte, Transformers: how machines are changing every sector of the UK economy, January 2016, pp.4-5 46

National Science and Technology Council, Artificial Intelligence, Automation, and the Economy, December 2016

Deloitte (2016) - Jobs at risk of automation by sector

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AI, Automation, and the Economy

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economic growth could be hampered by rising inequality as the Bank of England and the IMF have recognised.

In terms of the effect on labour relations, job-specific skills may become redundant, people may change jobs more frequently and the increasing precariousness of work may be exacerbated. Working in the ‘gig economy’ - characterised by a rise in self-employment, temporary positions and contract work (the ‘contingent workforce’) - may become the norm for an increasing number of people. This will fundamentally alter traditional employment relationships and may restrict worker’s ability to reap the rewards of potential increases to productivity and economic growth.

The possibility of work displacement, heightened inequality and difficulty of integrating untrained workers into the ‘new’ job market has led to suggestions that policy interventions will be needed so AI’s economic benefits are broadly shared. In a draft report to the European Parliament, MEP Mady Delvaux said that there is a ‘need to introduce corporate reporting requirements on the extent and proportion of the contribution of robotics and AI to the economic results of a company for the purpose of taxation and social security contributions’47. Others suggest introducing a universal basic income, defined by the House of Commons Library as ‘a basic minimum income […] paid to all citizens by the state, without any conditions attached and regardless of their other resources’48.

47

Shiller, R., Why robots should be taxed if they take people's jobs, Guardian, March 2017 48

House of Commons Library, Universal basic income, September 2016

In Focus: Universal Basic Income

A universal basic income (UBI) is a fixed amount, at a level sufficient for living, given

by the state to all citizens irrespective of income or work status. It would replace at

least part of the existing welfare system and would involve a profound change in the

way income support is organised. Critics argue that UBI is financially and morally

irresponsible as it awards all citizens payments regardless of income, which may

negatively impact the UK economy. Unconditional payments are said to disincentivise

individuals from actively seeking paid employment, though it is noteworthy that such

claims are not substantiated by recent trials of UBI in Finland and Kenya. Advocates

argue that the disruptive impact of automation to a range of occupations will

necessitate a UBI model which would create a more robust safety net, give workers

more bargaining power in the market, ensure AI gains are more fairly distributed,

reduce reliance on means testing and help ameliorate problems of low take-up,

poverty and the stigma associated with welfare.

Ranelagh Political Communications

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This briefing was compiled by Ranelagh Political

Communications.

Ranelagh is a public affairs and policy development consultancy.

We work throughout the UK and in Europe, offering unrivalled

political expertise at the national, regional and local levels.

We combine keen political intelligence with a comprehensive

understanding of each client's business. This makes our

strategic counsel tailored, targeted and accurate. It makes our

advocacy effective.

If you want more details on any of the issues contained within

this briefing, or more information about the work of Ranelagh

Political Communications, contact Anna Wolffe on 020 3642

2754 or anna.wolffe@ranelaghuk.com.