Evidence Synthesis: The Impact Of Artificial Intelligence .
The impact ofartificial intelligenceon workAn evidence synthesis on implicationsfor individuals, communities, and societies
Contents1Executive summary4Introduction71.1Safely and rapidly harnessing the power of AI81.2Policy debates about automation and the future of work82The Royal Society and British Academy’s evidencesynthesis on AI and work113The impact of AI on economies and work153.1164AI has significant economic potential3.2 AI-enabled changes could affect the quantity and quality of work3.2.1 Concerns about automation and the workplace have a long history3.2.2 Studies give different estimates of the number of jobs affected by AI3.2.3 Jobs and tasks may be affected by AI in different ways3.2.4 Commercial, social, and legal factors may influence AI adoption17181923243.3 The impact of technology-enabled change on economies and employment3.3.1 Forces shaping the impact on technology on economiesand the structure of employment3.3.2 AI technologies may also affect working conditions3.3.3 How might the benefits of AI be distributed?2626Discussion393134
4 THE IMPACT OF ARTIFICIAL INTELLIGENCE ON WORKExecutive summaryArtificial intelligence (AI) technologies areadministrative data and more detailed informa-developing apace, with many potential benefitstion on tasks has helped improve the reliability offor economies, societies, communities and indi-empirical analysis. This has reduced the reliance onviduals. Across sectors, AI technologies offer theuntested theoretical models and there is a growingpromise of boosting productivity and creating newconsensus about the main types of jobs thatproducts and services. Realising their potentialwill suffer and where the growth in new jobs willrequires achieving these benefits as widely asappear. However, there remain large uncertaintiespossible, as swiftly as possible, and with asabout the likely new technologies and their precisesmooth a transition as possible.relationship to tasks. Consequently, it is difficult tomake precise predictions as to which jobs will see aThe potential of AI to drive change in manyfall in demand and the scale of new job creation.employment sectors has revived concerns overautomation and the future of work. While muchThe extent to which technological advances are –of the public and policy debates on AI and workoverall – a substitute for human workers dependshave tended to oscillate between fears of the ‘endon a balance of forces, including productivityof work’ and reassurances that little will change ingrowth, task creation, and capital accumulation.terms of overall employment, evidence suggestsThe number of jobs created as a result of growingneither of these extremes is likely. However, theredemand, movement of workers to different roles,is consensus that AI will have a disruptive effectand emergence of new jobs linked to the newon work, with some jobs being lost, others beingtechnological landscape all also influence thecreated, and others changing.overall economic impact of automation byAI technologies.There are many different perspectives on ‘automatability’, with a broad consensus that current AIWhile technology is often the catalyst for revis-technologies are best suited to ‘routine’ tasks,iting concerns about automation and work, andalbeit tasks that may include complex processes,may play a leading role in framing public and policywhile humans are more likely to remain dominantdebates, it is not a unique or overwhelming force.in unpredictable environments, or in spheres thatOther factors also contribute to change, includingrequire significant social intelligence.political, economic, and cultural elements.Over the last five years, there have been manyStudies of the history of technological changeprojections of the numbers of jobs likely to be lost,demonstrate that, in the longer term, technologiesgained, or changed by AI technologies, with varyingcontribute to increases in population-leveloutcomes and using various timescales for analysis.productivity, employment, and economicMost recently, a consensus has begun to emergewealth. But these studies also show that suchfrom such studies that 10–30% of jobs in the UKpopulation-level benefits take time to emerge, andare highly automatable. Many new jobs will alsothere can be periods in the interim when parts ofbe created. The rapid increase in the use ofthe population experience significant disbenefits.
EXECUTIVE SUMMARY 5Substantial evidence from historical and contem-are disproportionately affected and benefitsporary studies indicates that technology-enabledare not widely distributed.changes to work tend to affect lower-paid andlower-qualified workers more than others. ThisThis evidence synthesis provides a review ofsuggests there are likely to be transitional effectsresearch evidence from across disciplines inthat cause disruption for some people or places.order to inform policy debates about theinterventions necessary to prepare for theIn recent years, technology has contributedfuture world of AI-enabled work, and to supportto a form of job polarisation that has favoureda more nuanced discussion about the impacthigher-educated workers, while removingof AI on work. While there are a number ofmiddle-income jobs,and increasing competitionplausible future paths along which AI tech-for non-routine manual labour. Concentration ofnologies may develop, using the best availablemarket power may also inhibit labour’s incomeevidence from across disciplines can help ensureshare, competition, and productivity.that technology-enabled change is harnessedto help improve productivity, and that systemsOne of the greatest challenges raised by AI isare put in place to ensure that any productivitytherefore a potential widening of inequality, atdividend is shared across society.least in the short term, if lower-income workers
CHAPTER 1Introduction
8 THE IMPACT OF ARTIFICIAL INTELLIGENCE ON WORKIntroduction1.1 Safely and rapidly harnessing the power of AIArtificial intelligence (AI) technologies are developing apace, with many potential benefits for economies, societies, communities, and individuals. Realising their potentialrequires achieving these benefits as widely as possible, as swiftly as possible, and withas smooth a transition as possible.Across sectors, AI technologies offer the promise of boosting productivity and creatingnew products and services. These technologies are already being applied in sectorssuch as retail, manufacturing, and entertainment, and there is significant potential forfurther uptake, for example in pharmaceuticals, education, and transport.1The UK is well-placed to take advantage of the opportunities presented. It hasglobally-recognised capability in AI-related research disciplines, has nurtured clustersof innovative start-ups, and benefits from a policy environment that has been supportive of open data efforts.1.2 Policy debates about automation and the future of workWith this potential, come questions about the impact of AI technologies on work andworking life, and renewed public and policy debates about automation and the future ofwork. There are already indications that such questions have entered public consciousness, with the British Social Attitudes 2017 survey showing that 7% of respondents felt“it is likely that many of the jobs currently done by humans will be done by machinesor computer programmes in 10 years’ time”, and public dialogues by the Royal Societyhighlighting ‘replacement’ as one area of concern about AI technologies for membersof the public.2In considering the potential impact of AI on work, a range of studies and authors havemade predictions or projections about the ways in which AI might affect the amount,type, and distribution of work. While strong consensus exists among scholars over1The Royal Society (2017). Machine learning: the power and promise of computers that learn by example.Retrieved from https://royalsociety.org/ ns/machinelearning-report.pdf/2Phillips, D., Curtice, J., Phillips, M. and Perry, J. (eds.) (2018), British Social Attitudes: The 35th Report, London:The National Centre for Social Research. Retrieved from alattitudes-35/key-findings.aspx
INTRODUCTION 9historical patterns, projections of future impacts vary, particularly quantitative onessuch as those estimating the number of job losses. Such studies indicate that there aremany plausible future paths along which AI might develop.Notwithstanding this significant uncertainty surrounding the future world of work,evidence from previous waves of technological change – including the Industrial Revolution and the advent of computing – can provide evidence and insights to inform policydebates today. Meanwhile studies from across research domains – from economicsto robotics to anthropology – can inform thinking about the role of different forces,actors, and institutions in shaping the role of technology in society.Though much of the public debate on AI and work has tended to oscillate between fearsof ‘the end of work’ and reassurances that little will change in terms of overall employment, evidence from across academic disciplines and research papers suggests neitherof these extremes is likely. Instead, there is consensus in academic literature that AI willhave a considerable disruptive effect on work, with some jobs being lost, others beingcreated, and others changing.In this context, two types of policy-related priorities emerge: Ensuring that technology-enabled change leads to improved productivity; and Ensuring that the benefits of such change are distributed throughout society.This synthesis of research evidence by the Royal Society and the British Academy drawson research across several disciplines – by economists, historians, sociologists, datascientists, law and management specialists, and other experts. It aims to bring togetherkey insights from current research and debates about the impact of AI on work, to helppolicy-makers to prepare for the impacts of change among different groups, and toinform strategies to help mitigate adverse impacts.33For the Royal Society, this project is part of a wider programme of policy activities on data and AI.More information about this work is available at this link: e-and-data
CHAPTER 2The Royal Society andBritish Academy’sevidence synthesison AI and work
12 THE IMPACT OF ARTIFICIAL INTELLIGENCE ON WORKThe Royal Society andBritish Academy’s evidencesynthesis on AI and workBuilding on the key messages of the Royal Society’s 2017 report on Machine Learning, in 2018,the Royal Society and British Academy convened leading researchers and policy experts toconsider the implications of AI-enabled technological change for the future of work.This evidence synthesis – which follows a programme of research and engagement with keyacademic and policy stakeholders – is designed to provide a digest of academic literatureand thinking on AI’s impact on work. It is based on a review of recent literature conductedby Frontier Economics, as well as two seminars attended by leading authors, scholars, andAI practitioners.4The Frontier Economics literature review, published alongside this paper, collected over 160relevant English-language documents published since 2000, across a wide range of disciplines.These included articles published in peer-reviewed journals and academic manuscripts, as wellas reports published by public sector organisations, international organisations, think-tanksand consultancies. A short list of 47 documents to be reviewed in detail was selected fromthe long list of 160, including evidence on historical and recent effects of technology on work;theoretical frameworks for considering AI’s future impacts; and specific projections on futureimpacts of AI. This literature review was complemented and informed by the workshops, andby interviews with leading thinkers and policy-makers.5 It was further refined by expert peerreview, within Frontier Economics6 and at the Royal Society and the British Academy.7The evidence synthesis that follows starts by noting the potential of AI across businesssectors and the current state of AI adoption, before exploring the different insights thatcome from across disciplines when considering the impact of AI on the overall amount ofwork and the quality of work available. It then considers the factors influencing the impactof AI on economies and societies, and the ways in which societies share the benefits ofthese technologies.4From 19–21 February 2018, The Royal Society and American Academy of Arts and Sciences co-hosted aworkshop exploring the impact of AI on working life. On 15 March 2018, The Royal Society and British Academyhosted a joint workshop on the subject ‘is this time different?’, exploring the economic and social implicationsof AI-enabled changes to work and the economy.5In compiling its review, Frontier Economics interviewed: Andrew Haldane, Chief Economist, Bank of England;Professor Stephen Machin, Director – Centre for Economic Performance, London School of Economics; GeoffMulgan, Chief Executive, Nesta; and Richard Susskind, IT Adviser to the Lord Chief Justice of England and Wales,and chairman of the Advisory Board of the Oxford Internet Institute.6By Sir Richard Blundell, David Ricardo Professor of Political Economy at University College London.7In addition to review by the project steering group, Frontier Economic’s work was reviewed by an externalreview group, consisting of: Professor Jon Agar, Professor of Science and Technology Studies, UCL; ProfessorPam Briggs, Professor of Applied Psychology, Northumbria University; Helen Ghosh, Master of Balliol College,Oxford; Professor Patrick Haggard, Professor of Cognitive Neuroscience, UCL; and Professor Nick Jennings,Professor of AI, Imperial.
THE ROYAL SOCIETY AND BRITISH ACADEMY’S EVIDENCE SYNTHESIS ON AI AND WORK 13This synthesis uses ‘Artificial Intelligence (AI)’ as an umbrella term for a suite of technologiesthat perform tasks usually associated with human intelligence. Machine learning is the technology responsible for driving most of the current and recent advances within the field of AI,and is a technology that enables computer systems to perform specific tasks intelligently, bylearning from data (see Box 1 for further details).BOX 1 Digital technology, automation, artificial intelligence and machine learningDigital technology refers to all forms of hardware and software using binary code to performtasks, from conventional spreadsheets or calculators on personal computers to networkedsystems and advanced algorithms that enablecomputer systems to make decisions basedon data analysis.Automation in its broadest sense is the replacement of human beings with machines, roboticsor computer systems to carry out an activity.The term can apply to the earliest mechanicaldevices, the changes seen in the IndustrialRevolution and assembly line manufacturing,as well as computing and robotics. In policydebates about artificial intelligence, automationis often used to refer to the migration of humantasks to computers and robots, whether or notAI technologies are necessary to enable this.Artificial intelligence (AI) is an umbrella termthat describes a suite of technologies that seekto perform tasks usually associated with humanintelligence. John McCarthy, who coined theterm in 1955, defined it as “the science and engineering of making intelligent machines.”8Machine learning is a branch of AI that enablescomputer systems to perform specific tasksintelligently. These systems carry out complexprocesses by learning from data, rather thanfollowing pre-programmed rules. Recent yearshave seen significant advances in the capabilitiesof machine learning, as a result of the increasedavailability of data; advanced algorithms; andincreased computing power. Many people nowinteract with machine learning-driven systemson a daily basis: in image recognition systems,such as those used to tag photos on socialmedia; in voice recognition systems, such asthose used by virtual personal assistants; and inrecommender systems, such as those used byonline retailers.9Today, machine learning enables computersystems to learn to carry out specific functions‘intelligently’. However, these specificcompetencies do not match the broad suiteof capabilities demonstrated by people.Human-level intelligence – or ‘general AI’ –receives significant media attention, but thisis still some time from being delivered, and it isnot clear when this will be possible.8McCarthy, J. (n.d.) What is artificial intelligence? Stanford University. Retrieved from: at-is-ai/index.html9The Royal Society, Machine learning report.
FIGURE 1An illustration of the relationships between automation, the digital revolution,and AI technologiesAI technologies, including machinelearning, are supporting productsand services across sectors.Digital technologies have alreadybrought significant changes towork, for example the use of wordprocessing, instead of typing.digitalrevolutionaiautomationAutomation can refer to a broad suiteof technologies, including the IndustrialRevolution and forms of mechanismacross sectors. Ploughing a field witha tractor instead of horses, for example.Not all automation is AI-enabled.For example, supermarketself-checkouts in placeof human operators.
CHAPTER 3The impact of AIon economiesand work
16 THE IMPACT OF ARTIFICIAL INTELLIGENCE ON WORKThe impact of AIon economies and work3.1 AI has significant economic potentialAI technologies are already supporting new products and services across a rangeof businesses and sectors: Intelligent personal assistants using voice recognition, such as Siri, Alexa, andCortana, are commonplace in many businesses. In the transport sector, AI processes underpin the development ofautonomous vehicles10 and are helping manage traffic-flows and design oftransport systems. In education, AI technologies are supporting personalised learning systems. In healthcare, AI is enabling new diagnostic and decision-support tools formedical professionals. In retail and logistics, AI is supporting the design of warehouse facilities toimprove efficiency. In development and humanitarian assistance, data analytics enabled by AI arehelping support the delivery of the Sustainable Development Goals and theassessment of humanitarian scenarios.11 In the creative industries, developers are creating computer systems that canproduce simple news reports, for example on business results,12 composeorchestral music,13 and generate short pieces of film.14 Across sectors, AI is being put to use to analyse vast quantities of data, toimprove business processes or design new services.Different AI technologies or applications are developing at different paces, and theiradoption across sectors and businesses is variable. A recent Stanford University study10Stone, P. et al. (2016) “Artificial Intelligence and Life in 2030.” One Hundred Year Study on Artificial Intelligence:Report of the 2015–2016 Study Panel, Stanford, CA: Stanford University.c Retrieved from: http://ai100.stanford.edu/2016-report11Vacarelu, F. (2018) Continuing the AI for good conversation: Takeaways from the 2018 AI for good globalsummit. United Nations Global Pulse. Retrieved from: Summit2018Takeaways12Lacity, M.C. & Willcocks, L.P. (2016) ‘A new approach to automating services’. MIT Sloan Management Review,58(1), 41. Retrieved from: http://eprints.lse.ac.uk/68135/1/Willcocks New%20approach 2016.pdf13Moss, R. (2015) Creative AI: Computer composers are changing how music is made. New Atlas magazine.Retrieved from: nce-computer-algorithmic-music/35764/14Hutson, M. (2018) New algorithm c
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