The Royal Society's The impact of artificial intelligence on work An evidence synthesis on implications for individuals, communities, and societies paper comments
Artificial intelligence (AI) technologies are developing apace, with many potential benefits for economies, societies, communities and individuals. Across sectors, AI technologies offer the promise of boosting productivity and creating new products and services. Realising their potential requires achieving these benefits as widely as possible, as swiftly as possible, and with as smooth a transition as possible.
The potential of AI to drive change in many employment sectors has revived concerns over automation and the future of work. While much of the public and policy debates on AI and work have tended to oscillate between fears of the ‘end of work’ and reassurances that little will change in terms of overall employment, evidence suggests neither of these extremes is likely. However, there is consensus that AI will have a disruptive effect on work, with some jobs being lost, others being created, and others changing.
There are many different perspectives on ‘automatability’, with a broad consensus that current AI technologies are best suited to ‘routine’ tasks, albeit tasks that may include complex processes, while humans are more likely to remain dominant in unpredictable environments, or in spheres that require significant social intelligence.
Over the last five years, there have been many projections of the numbers of jobs likely to be lost, gained, or changed by AI technologies, with varying outcomes and using various timescales for analysis. Most recently, a consensus has begun to emerge from such studies that 10–30% of jobs in the UK are highly automatable. Many new jobs will also be created. The rapid increase in the use of administrative data and more detailed information on tasks has helped improve the reliability of empirical analysis. This has reduced the reliance on untested theoretical models and there is a growing consensus about the main types of jobs that will suffer and where the growth in new jobs will appear. However, there remain large uncertainties about the likely new technologies and their precise relationship to tasks. Consequently, it is difficult to make precise predictions as to which jobs will see a fall in demand and the scale of new job creation.
The extent to which technological advances are – overall – a substitute for human workers depends on a balance of forces, including productivity growth, task creation, and capital accumulation. The number of jobs created as a result of growing demand, movement of workers to different roles, and emergence of new jobs linked to the new technological landscape all also influence the overall economic impact of automation by AI technologies.
While technology is often the catalyst for revisiting concerns about automation and work, and may play a leading role in framing public and policy debates, it is not a unique or overwhelming force. Other factors also contribute to change, including political, economic, and cultural elements.
Studies of the history of technological change demonstrate that, in the longer term, technologies contribute to increases in population-level productivity, employment, and economic wealth. But these studies also show that such population-level benefits take time to emerge, and there can be periods in the interim when parts of the population experience significant disbenefits.
Substantial evidence from historical and contem- porary studies indicates that technology-enabled changes to work tend to affect lower-paid and lower-qualified workers more than others. This suggests there are likely to be transitional effects that cause disruption for some people or places.
In recent years, technology has contributed to a form of job polarisation that has favoured higher-educated workers, while removing middle-income jobs,and increasing competition for non-routine manual labour. Concentration of market power may also inhibit labour’s income share, competition, and productivity. One of the greatest challenges raised by AI is therefore a potential widening of inequality, at least in the short term, if lower-income workers are disproportionately affected and benefits are not widely distributed.
This evidence synthesis provides a review of research evidence from across disciplines in order to inform policy debates about the interventions necessary to prepare for the future world of AI-enabled work, and to support a more nuanced discussion about the impact of AI on work. While there are a number of plausible future paths along which AI technologies may develop, using the best available evidence from across disciplines can help ensure that technology-enabled change is harnessed to help improve productivity, and that systems are put in place to ensure that any productivity dividend is shared across society.
