Workers have been told to ‘retrain’ for AI – but there’s a problem
As more companies and governments adopt AI, a common rhetoric which has emerged is that employees across a number of sectors will need to retrain—either to take advantage of the technology or to find completely new roles which are not replaced by AI.
Yet there has been comparatively little discussion about what these programs look like and their feasibility, notes researchers at the Brookings Institute think tank.
“The evidence that does exist, however, provides reasons for policymakers to be sceptical of retraining as a means of supporting labour adjustment to AI-enabled automation,” it said.
“For retraining to keep up with AI advancements, we may need to fundamentally rethink how we provide it, study its effects, formulate its overarching goals, and understand its limitations.”
A question of jobs
First, there is simply the question of whether there will be enough jobs for workers to retrain into, the group said. Although there is no evidence to suggest that technological shocks result in a systemic and prolonged increase in the unemployment rate, there is evidence suggesting that it may result in short- to medium-term reductions in the number of ‘skilled’ occupations for workers to retrain into.
The question for workers looking to retrain is typically not, “How do I find employment?” but rather “How do I upskill to access more desirable employment with job security?”.
“Literature suggests that technological change can result in a scenario where the supply of ‘skilled workers’ is higher than the number of ‘skilled’ middle-wage jobs that are available,” the Brookings Institute said.
“For instance, there is evidence that displaced robotics workers ended up in lower-paid service jobs. These periods of mismatch can be damaging to livelihoods, leaving long-lasting impacts on families and communities. This is also why it is important not to read too much into the evidence on private sector training programs.”
Although the evidence on these sectoral initiatives is overwhelmingly positive, we do not know if these impacts will generalise. And while they may shift who has access to available ‘good jobs’, they may not have any impact on the availability of ‘good work’, the group said.
A question of reskilling
A second challenge for worker retraining is that many people simply may not be able or willing to reskill, the group said.
“Worker training invariably involves workers incurring the costs, in terms of time and/or the opportunity cost of forgoing labour income, of technological change.”
It adds that participants in worker retraining programs in the US are disproportionately likely to come from the most vulnerable backgrounds, such as homelessness, criminal offences, and/or single parenthood.
“Evidence suggests that classroom time learning new skills may not be an effective policy response, particularly if workers experience other serious social and health concerns. And older people, a group very likely to be overrepresented in jobs at risk of automation due to digitalisation, may not be interested in retraining, particularly for those closer to retirement.”
Moreover, literature has previously suggested these obstacles to retraining only increase as the economy becomes more specialised, as this entails greater barriers to entry in new professions as well as skillsets that may not be as easily transferrable to other occupations, the group said.
A question of demand
A final challenge confronting worker retraining programs is uniquely problematic amid AI, the group said.
“Reskilling program organisers frequently cite issues anticipating future labour market demands; very often, workers appear to retrain from one automation-susceptible occupation to another. This is a problem that is ubiquitous in research attempting to anticipate AI’s impacts.”
As a result, even retraining organisations have a foggy understanding of AI’s future economic impact, the Institute said. This makes it difficult for retraining organisations to identify areas to focus for reskilling, potentially leading to investment in the wrong types of training.
“Indeed, local retraining programs consistently confront the perilous task of needing to work with employers to anticipate the jobs that will be in demand, not just today and in coming months, but also in the next several years.”
As AI advances, things are likely to get only more complex, especially if one is willing to entertain the notion that AI could present a uniquely disruptive shock, the group warned.
“Although previous technologies induced higher inequalities and often painful transitory periods, they ultimately created more jobs than they destroyed. This may not be the trajectory that AI follows,” it said.
“The potential for more advanced machine learning systems to automate core human cognitive functions could kindle extremely rapid labour substitution, resulting in a world where there is a dramatic machine replacement of decent work. Even if new, good work were to eventually emerge, a scenario with rapid labour substitution could make retraining extraordinarily difficult.”
