Is AI really a paradigm shifting phenomenon for how we work?
Artificial Intelligence is predicted by some to change the way we work and triggers large-scale restructuring of the UK labour market. We will need new skills, abilities and ways of learning to be more adaptable. How can we respond to this change — and are there lessons from the past to learn from?
Artificial Intelligence (AI) is framed as a driving factor of the fourth industrial revolution that is said to fundamentally transform the UK labour market. Jobs are increasingly affected by emerging technologies like AI that can automate work processes and consequently have the power to both replace and create jobs. Outlooks on the future vary between prospects of an AI-enabled labour market that will make our lives better by working less and more grim predictions of job loss, difficult retraining processes and exacerbated inequalities.
“There will certainly be jobs that go and there will certainly be many, many jobs that are dramatically changed by automation, but we think that the loss of jobs will be accompanied by the creation of new jobs. We don’t think we’re going to be in a world where everybody works for four hours a week.” — Stephen, Timms (Chair of the Work and Pensions Select Committee)
Surely we will be navigating a reality between these two contrasting visions, but the current view of the UK government expressed in the newly published National AI Strategy reflects a willingness to embrace AI and the structural changes it implies for the UK labour market. Large scale investments into conversion Masters, re-training programs and excellence programs are part of a wider strategy to make the UK economy, and its labour market, ready and competitive for an AI-enabled future. Re-training, upskilling and lifelong learning are the new buzz words dominating the discussion about how to prepare the workforce for this new work environment.
What future skills are needed and how can AI and digital literacy be increased amongst the population? How can we make sure that access to AI-related training and education is equally distributed across industries and regions? What strategies are necessary to ensure that AI in the field of work doesn’t exacerbate social inequalities? All these questions must be answered for developing a holistic AI-skill strategy for the UK labour market. This raises the question of whether these challenges are new, and an isolated phenomenon caused by AI as a technology, or if such challenges are more universal for structural change to the labour market. Are there similarities with previous industrial revolutions and restructuring processes that we could learn from?
If we think of the first Industrial Revolution 200 years ago, one of the main drawbacks was the alienation of workers through the increased use of machines and a deterioration of their working conditions. In our current era of the gig-economy — with riders delivering food and other commodities under precarious circumstances using algorithmically powered applications that may take arbitrary decisions — it seems as though patterns of alienation and poor working conditions are repeating themselves. But is the analogy this straightforward?
Initially, the Industrial Revolution led to deteriorating working conditions with lower wages, large-scale layoffs, greater health risks and a redefinition of social communities and solidarity schemes. Only in a gradual and time-consuming process was it possible to ensure workers’ rights and to create meaningful alternatives for lost jobs. Today, we have the necessary social and economic regulations to prevent such large-scale negative effects potentially caused by wholesale introduction of AI to the workplace, but current trends suggest otherwise. Examples in diverse industries show that there is a systematic hollowing out of social and economic rights to serve the fast-paced service on demand-based development of the market.
Even though some argue new technologies raise overall labour productivity and increase the general standard of living in the long run, just as during previous industrial revolutions and restructuring processes, the employees most closely affected by change are often the ones to absorb negative effects like precarious working conditions and social insecurity. How can such effects be effectively limited and how can the workforce — and especially its most vulnerable members — be empowered and prepared for change? How can future skills and broader social conditions be improved across the scale?
One lesson from previous technological transitions can be learned from the movement for a “just transition process” that first emerged in the US when high-carbon technologies were to be replaced by low-carbon ones. In this context, the process was considered as a means to break the pattern of sacrificing the well-being and working standards of vulnerable groups during labour restructuring processes induced by technological change. The focus was on putting justice and equity at the centre of the transition through ambitious social and economic restructuring addressing the roots of inequality.
Considering the broader implications for employees caused by the shift towards AI-driven work contexts — ranging from potential job and retraining needs to general demand for up-skilling and lifelong learning — could the model of a “just transition” be a useful framework to apply to the future of work in the world of AI?
As the implications and insecurities for the workforce are largely comparable, some of the main points of the Just Transition framework could be valuable for considerations on a holistic AI transition and skill strategy.
The framework suggests not only “research and early assessment of social and employment impacts”, but also focuses on fostering “social dialogue and democratic consultation of social partners and stakeholders”. This open social dialogue in the field of AI implies a larger consultation of the workforce itself to see how the people most affected by AI see the future of their work and what skills they think they can develop and under what conditions. This could then help inform “active labour market policies and regulation, including training and skills development”. Such open and inclusive discussions on policy could help readapt and refocus existing skill programmes such as Conversion Masters or continuous professional development programs according to the needs and conditions articulated by the workforce itself.
As these examples show, past industrial revolutions and restructuring processes can indicate what can go wrong. but also how to design changes to the labour market in inclusive and just ways, helping us move towards an AI-enabled workforce with more ease. AI might shift the paradigm in how we work and live but the effects and restructuring processes it provokes are not unique. This can motivate us to implement good lessons from the past and prepare us not to replicate historical injustices.