Taccle AI

Improving skills and competences of VET teachers and trainers in the age of Artificial Intelligence

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How are jobs sensitive to AI doing: an update

Graham Attwell 34 Views 0 Comments , , , , , ,
Nacho Kamenov & Humans in the Loop / Better Images of AI / Data annotators discussing the correct labeling of a dataset / CC-BY 4.0

‭Jeisson Cardenas-Rubio and Gianni Anelli-Lopez from the University of Warwick Institute for Employment Research have posted an interesting blog on the LMi for All website. They have been using data from scraped job adverts to assess the impact of generative AI on employment in the UK. In their first report, some six months ago and looking at data until mid 2022, they looked at the impact on 15 jobs identified by Eloundou, (2023) as vulnerable to Chat GPT. In their initial research they found that "although the fear of AI-induced job displacement is understandable, the current evidence from the UK suggests that AI tools such as Chat-GPT are not yet leading to job losses. The initial findings from the United Kingdom (UK) indicate that, following the launch of Chat-GPT, there have been no sizable changes in the labour market trends, particularly for jobs deemed susceptible to these type of AI tools."

The follow up research suggests some change. In the period to December 2023, the data

begins to reveal a subtle yet persistent decline in the share of online job advertisements (OJAs) for jobs considered to be sensitive to the diffusion of GPTs (i.e. jobs where tasks can be automated or augmented by the widespread adoption and integration of GPTs).

They question whether the modest negative trajectory will continue, stabilise at its current levels, or whether there will be a resurgence as the market adapts and finds new equilibrium with AI technologies?

In conclusion they say:

Incorporating the insights of Acemoglu, Autor, and Johnson (2023), we recognise that selecting a path where technology complements human skills is possible but demands shifts in technological innovation, corporate norms, and behaviours. The goal is to use generative AI to develop new tasks and enhance capabilities across various professions, including teaching, nursing, and technical trades. This approach can help reduce inequality, increase productivity, and elevate wages by enhancing the skill level and expertise of workers.

References

Acemoglu, D. et al. (2023) Can we Have Pro-Worker AI? Choosing a path of machines in service of minds. Centre for Economic Policy Research. Available at: https://cepr.org/system/files/publication-files/191183-policy_insight_123_can_we_have_pro_worker_ai_choosing_a_path_of_machines_in_service_of_minds.pdf

Eloundou, T. et al. (2023) ‘GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models’. Available at: http://arxiv.org/abs/2303.10130.

Appendix

This is the fifteen occupations identified by Eloundou as sensitive to AI. The numbers refer to the UK Standard Occupational Classification: it would be interesting to hear from any similar work undertaken in other European countries.

  1. Survey Researchers – Business and related research professionals (UK SOC code 2434)
  2. Animal Scientists – Biological scientists (2112)
  3. Climate Change Policy Analysts – Social and humanities scientists (2115)
  4. Blockchain Engineers – Programmers and software development professional (2134)
  5. Web and Digital Interface Designers – Web design professionals (2141)
  6. Financial Quantitative Analysts – Finance and investment analysts and advisers (2422)
  7. Tax Preparers – Taxation experts (2423)
  8. Mathematicians – Actuaries, economists and statisticians (2433)
  9. News Analysts, Reporters, and Journalists – Newspaper and periodical journalists and reporters (2492)
  10. Public Relations Specialist – Public relations professionals (2493)
  11. Proofreaders and Copy Markers – Authors, writers and translators (3412)
  12. Accountants and Auditors – Book-keepers, payroll managers and wages clerks (4122)
  13. Correspondence Clerks – Records clerks and assistants (4131)
  14. Clinical Data Managers – Database administrators and web content technicians (3133)
  15. Court Reporters and Simultaneous Captioners – Typists and related keyboard occupations (4217).

(Eloundou T. et al. (2023) ‘GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models’. Available at: http://arxiv.org/abs/2303.10130.

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Jobs of 270000 shopworkers in UK at threat from AI

Graham Attwell 609 Views , , , ,

Photo by Nathália Rosa on Unsplash

I'm old enough to remember the first supermarket, Fine Fare, arriving in my hometown, Swindon. I guess it was sometime in the 1950s. And I remember prior to that my mother queuing in the grocery store for a counter assistant who would go and get whatever was on the shopping list (as long as we had sufficient ration coupons). Of course supermarkets totally changed the world of shops and as importantly the work of the employees.

Fast forward to the 1990s. I won a bet (for three beers) with my colleague Professor Gerald Heidegger who assured me that bar codes would never be accepted in German supermarkets by other staff or customers (he also predicted the failure of the new extended opeing hours for supermarkets that has previously had to close at 1230 on Saturdays).

All this is a prelude to predictions for the latest revolution in shopping, discussed in an article, 'Do smart supermarkets herald the end of shopping as we know it?', by Rupert Neate in yesterdays Observer newspaper. The article reports on how the major supermarket chains in the UK - and significantly also Amazon - are trialing cashless shops. Entry will be through a dedicated app, and with the use of hundreds of sensors and cameras and an AI based software platform, shopper will be charged through their credit card and receive a receipt on their mobile phone on exiting the store.

The new systems are claimed to be a response to shoppers dislike of queuing although more likely they are an attempt to reduce costs both by reducing shoplifting (which is said to be rampant with new self checkout systems) and through cutting the numbers of shop workers employed. "While fitting out supermarkets with the new technology costs about £1m-per-store, the firms installing it claim it will pay for itself within 18 months because it will hopefully eliminate theft."

The Observer draws attention to the danger of exclusion for those lacking a smartphone. Emmanuelle Andrews, policy and campaigns officer at human rights group Liberty is quoted as saying: “Shopping should be one of the great levellers, where the businessman in the sharp suit is shoulder-to-shoulder with the pensioner on benefits. Everyone has to buy food, but with this technology only those with a smartphone and credit cards will be able to shop there.”

But if the new system catches on the big impact will be on jobs. "The checkout-free technology is specifically designed to eliminate jobs, and save money,” the artcile quotes Dr Carl Benedikt Frey, an Oxford University economist and expert on automation. "Frey fears that a national rollout of AI stores would send the checkout worker the same way as the elevator operator, which as of today is the only one of 270 job descriptions listed in the 1950 US census to be completely eliminated by automation."  AI is a threat to the jobs of 270,000 checkout workers in the UK, most of whom are women. The Office for National Statistics (ONS) has identified supermarket cashier jobs as among the most at risk of being replaced by automation, with 65% of checkout-operator jobs said to be at risk. And overall, The ONS analysis shows that 70.2% of the roles at high risk of automation are currently held by women.

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Digitalisation, Artificial Intelligence and Vocational Occupations and Skills

Graham Attwell 743 Views , , , , , , , , , ,
The Taccle AI project on Artificial Intelligence and Vocational Education and Training, has published a preprint  version of a paper which has been submitted of publication to the VET network of the European Research Association. The paper, entitled  Digitalisation, Artificial Intelligence and Vocational Occupations and Skills: What are the needs for training Teachers and Trainers, seeks to explore the impact AI and automation have on vocational occupations and skills and to examine what that means for teachers and trainers in VET. It looks at how AI can be used to shape learning and teaching processes, through for example, digital assistants which support teachers. It also focuses on the transformative power of AI that promises profound changes in employment and work tasks. The paper is based on research being undertaken through the EU Erasmus+ Taccle AI project. It presents the results of an extensive literature review and of interviews with VET managers, teachers and AI experts in five countries. It asks whether machines will complement or replace humans in the workplace before going to look at developments in using AI for teaching and learning in VET. Finally, it proposes extensions to the EU DigiCompEdu Framework for training teachers and trainers in using technology. The paper can be downloaded here.
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Pathways to Future Jobs

Graham Attwell 955 Views , , , , , ,

katielwhite91 (CC0), Pixabay

Even before the COVIP 19 crisis and the consequent looming economic recession labour market researchers and employment experts were concerned at the prospects for the future of work due to automation and Artificial Intelligence.

The jury is still out concerning the overall effect of automation and AI on employment numbers. Some commentators have warned of drastic cuts in jobs, more optimistic projections have speculated that although individual occupations may suffer, the end effect may even be an increase in employment as new occupations and tasks emerge.

There is however general agreement on two things. The first is that there will be disruption to may occupations, in some cases leasing to a drastic reduction in the numbers employed and that secondly the tasks involved in different occupations will change.

In such a situation it is necessary to provide pathways for people from jobs at risk due to automation and AI to new and hopefully secure employment. In the UK NESTA are running the CareerTech Challenge programme, aimed at using technology to support the English Government’s National Retraining Scheme. In Canada, the Brookfield Institute has produced a research report ‘Lost and Found, Pathways from Disruption to Employment‘, proposing a framework for identifying and realizing opportunities in areas of growing employment, which, they say “could help guide the design of policies and programs aimed at supporting mid-career transitions.”

The framework is based on using Labour Market Information. But, as the authors point out, “For people experiencing job loss, the exact pathways from shrinking jobs to growing opportunities are not always readily apparent, even with access to labour market information (LMI).”

The methodology is based on the identification of origin occupations and destination occupations. Origin occupations are jobs which are already showing signs of employment. Decline regardless of the source of th disruption. Destination jobs are future orientated jobs into which individuals form an origin occupation can be reasonably expected to transition. They are growing, competitive and relatively resilient to shocks.

Both origin and destination occupations are identified by an analysis of employment data.

They are matched by analysing the underlying skills, abilities, knowledge, and work activities they require. This is based on data from the O*Net program. Basically, the researchers were looking for a high 80 or 90 per cent match. They also were looking for destination occupations which would include an increase in pay – or at least no decrease.

But even then, some qualitative analysis is needed. For instance, even with a strong skills match, a destination occupation might require certification which would require a lengthy or expensive training programme. Thus, it is not enough to rely on the numbers alone. Yet od such pathways can be identified then it could be possible to provide bespoke training programmes to support people in moving between occupations.

The report emphasises that skills are not the only issue and discusses other factors that affect a worker’s journey, thereby, they say “grounding the model in practical realities. We demonstrate that exploring job pathways must go beyond skills requirements to reflect the realities of how people make career transitions.”

These could include personal confidence or willingness or ability to move for a new job. They also include the willingness of employers to look beyond formal certificates as the basis for taking on new staff.

The report emphasises the importance of local labour market information. That automation and AI are impacting very differently in different cities and regions is also shown in research from both Nesta and the Centre for Cities in the UK. Put quite simply in some cities there are many jobs likely to be hard hit by automation and AI, in other cities far less. Of course, such analysis is going to be complicated by COVID 19. Cities, such as Derby in the UK, have a high percentage of jobs in the aerospace industry and these previously seemed relatively secure: this is now not so.

In this respect there is a problem with freely available Labour Market Information. The Brookfield Institute researchers were forced to base their work on the Canadian 2006 and 2016 censuses which as they admit was not ideal. Tn the UK data on occupations and employment from the Office of National Statistics is not available at a city level and it is very difficult to match up qualifications to employment. If similar work is to be undertaken in the UK, there will be a need for more disaggregated local Labour Market Information, some of it which may already be being collected through city governments and Local Economic Partnerships.