This report explores future automation and creativity in the UK and US workforces. The authors find that creative jobs will be much more resistant to automation than most other jobs. Twenty-one per cent of US employment is highly creative - that is, has a probability of more than 70 per cent of being creative. These creative occupations include artists, architects, web designers, information technology (IT) specialists and public relations professionals. Relative to the United States, the UK has a higher fraction of creative employment, constituting around 24 per cent of the workforce. Given t
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This report explores future automation and creativity in the UK and US workforces. The authors find that creative jobs will be much more resistant to automation than most other jobs. Twenty-one per cent of US employment is highly creative - that is, has a probability of more than 70 per cent of being creative. These creative occupations include artists, architects, web designers, information technology (IT) specialists and public relations professionals. Relative to the United States, the UK has a higher fraction of creative employment, constituting around 24 per cent of the workforce. Given the broad concept of creativity adopted in the analysis, the authors estimates of creative employment are, as expected, bigger than official estimates.
The results strongly support the intuition that creative occupations are more future-proof to technologies like machine learning and mobile robotics. In the US, 86 per cent of workers in the highly creative category are found to be at low or no risk of automation. In the UK, the equivalent number is 87 per cent. At the regional level, it can be seen that places with a higher proportion of the workforce in creative jobs, most obviously London, are also more immune to automation. Economies like the UK and US where creative occupations make up a large proportion of the workforce may be better placed than others to resist the employment fallouts from future advances in computerisation. These findings reflect the fact that machines can most successfully emulate humans when a problem is well specified in advance - that is, when performance can be straightforwardly quantified and evaluated - and when the work task environment is sufficiently simple to enable autonomous control. They will struggle when tasks are highly interpretive, geared at 'products whose final form is not fully specified in advance', and when work task environments are complex - a good description of most creative occupations.
Edited excerpts from publisher's website.
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