• Opinion
  • December 12, 2018
  • 8 minutes
  • 2

Want to know what future job skills we need? Machine learning can help

Opinion: Sweden and Finland are using algorithms and open data to predict the future of work

This piece was written by Jack Orlik, a senior researcher at Nesta. For more like this, see our future of work newsfeed.

How can governments foster a society in which everyone can benefit from digitalisation?

Questions like this drive Digital Frontrunners, an innovative program launched by Nesta in 2018 to help governments from Belgium, Denmark, Estonia, Finland, the Netherlands and Sweden to make policies that are smarter, more inclusive and fit for the future.

Over the last seven months, the program has brought together more than 80 international participants from governments, unions, universities and social enterprises to discuss the challenges of digitalisation and learn new methods for policy design that can help to address them.

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The process has not only enabled attendees to make discoveries and connections that will help them in their work; it has also revealed common priorities, problems and successes that can inform the development of a more prosperous and inclusive digital economy.

What have we found out?

First, the digital strategies of these “Digital Frontrunner” countries emphasise the need for citizens to learn new skills that will enable them to adapt to the future of work. Second, they are actively exploring new approaches to create effective policies for skills. Third, they share four interlocking challenges that must be prioritised to enable all citizens to benefit from digital transformation and ensure that there are enough skilled workers for a changing labour market.

This blog looks at the first of these four challenges: anticipating the skills people need for the jobs of the future.

Governments should facilitate timely skills anticipation

It is now well-known that technological change is disrupting the labour market. We can see that demand for people with technical skills is increasing (often outstripping supply). But that is just part of the story. What’s less apparent are the specific effects that the application of new tools have on existing jobs.

As tasks are automated, the jobs of people who currently perform them will change. For some, this will mean taking on new responsibilities within their current workplace. Others will need to find new jobs in unfamiliar industries.

In either eventuality, people throughout the workforce are likely to need new skills; a recent survey of firms conducted by the World Economic Forum found that 54% of all employees will require extensive upskilling or reskilling by 2022.

Anticipating which skills will be valuable is therefore a priority for governments working to foster adaptable labour markets in which everyone can participate. The pace of change is fast: 43% of employees in the EU have experienced a change in technology at work in the past five years.

Machine learning can help

In this era of rapid technological change, established methods for skills anticipation, like employer surveys, can be too slow. Governments and organisations have begun exploring new ways to predict changing demand and supply of skills in a more timely manner.

One innovative approach makes use of job advertisement data and machine learning algorithms. Researchers at Nesta used 41 million job adverts to examine the digital skills required in different occupations, and compared demand for these skills to each occupation’s prospects for growth.

The digital skills most likely to be needed in growing job sectors are ones that are used in non-routine tasks

They found that the digital skills most likely to be needed in growing job sectors are ones that are used in non-routine tasks, problem-solving and the creation of digital outputs. By contrast, digital skills for clerical tasks are set to experience shrinking demand. Not all digital skills, then, are created equal.

The research is significant because it suggests that investment in training should be led by a close examination of the value of specific digital skills. And, it should support the development of complementary skills such as creative thinking. Otherwise, people risk investing in certain digital skills that have limited applications and may become redundant in the not-too-distant future.

What are the Digital Frontrunners doing?

Through the Digital Frontrunners program, we found that similar approaches to skills anticipation and job matching are being applied in Finland and Sweden.

In Finland, the Ratkaisu 100 challenge prize run by Sitra, the parliament’s innovation fund, awarded €500,000 ($567,000) to Headai, an organisation that uses artificial intelligence to map skills needs and provision. Headai’s “micro-competencies” service analyses around 1,000 job advertisements daily to identify which skills employers are looking for. It also aims to map the skills that are available in the workforce by drawing from various sources of open data available online.

In 2017, the Public Employment Service (PES) in Sweden launched Jobtech, a platform that provides access to an array of labour market datasets compiled by the agency. These include occupation forecasts, current and historical job adverts posted through the PES, and a dynamic competence map created by algorithmically analysing 6.3 million job adverts.

The Jobtech team is developing new ways to collect and analyse labour market data by using artificial intelligence, and is honing its methods in collaboration with PES analysts.

Applying machine learning to job adverts can provide a continuous and granular analysis of the skills that are needed by employers. The method is still in development and is limited by biases in the available data — in the UK, for example, job advertisements tend to be biased towards high-skilled roles. But it undoubtedly demonstrates a promising new avenue for skills anticipation in the digital age. — Jack Orlik

To read more, take a look at Nesta’s report: Digital Frontrunners: Designing inclusive skills policy for the digital age.

(Picture credit: Unsplash)


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