The threat that recent advances in job automation, machine learning and artificial intelligence (AI) represent for human workers is becoming more and more relevant in the economic and social debate.
CEOs like John Cryan of Deutsche Bank clearly state that robots can replace thousands of workers. In two different interviews, Cryan said that “in our banks, we have people behaving like robots doing mechanical things. Tomorrow we’re going to have robots behaving like people” and that “we’re too manual, which can make you error-prone and it makes you inefficient. There’s a lot of machine learning and mechanisation that we can do”.
Job losses triggered by technological innovation can have terrible social implications. Indeed, employment characterised by low wages, few benefits and high turnover rates, has provided for a kind of safety net for unskilled workers who have few other available options. In fact, these jobs traditionally offer an income of last resort when no better alternatives are accessible. Unfortunately, as robotics and advanced self-service technologies are increasingly deployed across nearly every sector of the economy, their continuous rise will primarily threaten lower-wage jobs that require a modest level of education and training. Subsequently, machines will try to compete with high-skill jobs as well, putting high-pay personnel at risk in the same way.
In recent years, many forums and seminars have been organised to discuss this issue. The re-alignment of the education system towards the skills that will presumably be required in the job market of the future is currently considered a solution. One assumes that the skills needed by future workers to succeed in the battle against machines will be different from the ones that make robots more proficient than humans (i.e. computation, inference and data mining).
Why This Time It’s Different
One of the lessons that history has taught us is that economic development is supported, if not caused, by technological innovation. Sometimes, innovation is so disruptive that it transforms not only production systems but also communities’ social structures; in other words, it can trigger an “industrial revolution”. What is happening today – as a consequence of advances in robotics and AI – is considered the “Fourth Industrial Revolution”.
The previous three industrial revolutions were characterised by a shift in the occupational structure of the labour market: from agriculture and handcraft boutiques to manufacturing and clerking, to services and management positions. The current one, which seeks to economise the use of labour through AI, machine learning and automation is actually outrunning the pace at which society can find alternative uses for labour. In brief, many more jobs are likely to be automated before the labour market is able to create replacement jobs.
While computerisation has historically been bounded into the domains of routine tasks, technological advances show that automation is likely to threaten not only mundane jobs, but also jobs featured by non-routine tasks.
As a matter of fact, if someone can learn to do a job by studying a detailed record of everything done in the past; or if someone can become proficient by simply repeating the tasks already completed, then there is a good chance that someday an algorithm might be able to learn to do much, or all, of that job. Furthermore, one cannot exclude that in the next decades some additional disruptive innovations will be introduced that could further accelerate the current replacement trend, especially if one considers quantum computers and their increasingly higher calculation capabilities.
A Call for Human Skills
In light of this, it seems clear that something must be changed in terms of the skills workers develop. However, in a world where automation is likely to absorb more and more jobs, what are the skills that will eventually prevent people from being replaced by machines?
In academia, there is a consensus around the tasks that machines will be unlikely to properly manage in the next two or three decades:
- Perception and manipulation tasks (e.g. finger dexterity, manual dexterity, mobility in cramped work space and awkward positions): despite the advances in terms of sensors and prehensile abilities, machines still struggle to identify objects in a cluttered field of view and to move in unstructured working environment.
- Creative intelligence tasks (e.g. originality, fine arts): creativity can be defined as a process that involves imagination or original ideas to produce something novel and valuable.
- Social intelligence tasks (e.g. social perceptiveness, negotiation, persuasion, assisting and caring for others): they involve the understanding of human needs, behaviours and emotions. Robots are expected to fail in these tasks in the next decades as well since human beings have a ‘common sense’ of information that is difficult to articulate in algorithms.
In addition, the human mind can identify causal relationships between two or more phenomena, while machines reason in terms of correlation. As such, the next generation of workers ought to enter the job market with the above-mentioned competencies, thus education and training must shift its focus towards them.
A Return to the Past?
But how shall education change in order to prevent new college graduates from being doomed to relatively unskilled jobs and future unemployment? First of all, by taking a route that is counterintuitive if we consider the current emphasis put on Information Technology, Math and Statistics. Education shall likely rediscover arts, humanities and social sciences because these are the academic disciplines that provide students with those creative, critical and problem-solving skills that really distinguish a human person from any kind of algorithm.
In fact, the study of these disciplines implies the development of skills such as speaking, writing, interpreting, relativising, comparing, distinguishing, telling the durable from the fleeting, clearly identifying different facts and understanding beauty, freedom, diversity and harmony. Finally, as all other ‘sciences’, those disciplines require observation, comparison, systematisation, inference and forecasting.
These assumptions are furtherly supported by the report “The Future of Jobs” issued by the World Economic Forum in 2016, which illustrates the 10 principal skills that will be required in 2020.
Source: World Economic Forum
Secondly, flexibility can be considered one of the keys for facing the reduction of jobs that machines are expected to bring. This is because the content of jobs is changing as fast as technology advances; therefore if education is focused on teaching students those skills that are unlikely to be mirrored by algorithms, then forthcoming workers will gain the flexibility to fit to new job contents. In brief, future workers shall be more concerned about having the skills required at work, rather than their qualifications. At the same time, employers shall succeed in identifying their employees’ real skills, irrespective of their formal qualifications, and adapt job content accordingly.
Finally, education systems shall include at least basic programming or coding courses in their subjects portfolio. In the coming decades, one can expect that the usage of machines and algorithms will be more frequent and relevant than today. Therefore, public and private institutions shall consider enabling all kind of workers (not only engineers) to directly communicate with machines by using programming languages.
Indeed, knowledge of machine language will allow individuals to understand the logic behind a machine’s algorithm; and consequently to control the machine by directly typing commands, rather than being completely dependent on the output of its ironclad algorithm.
The Future Ahead
The current industrial revolution led by robots and AI threatens the future of work for individuals. Compared to previous industrial revolutions, this one is not presenting the “creative destruction” phenomenon mentioned by Schumpeter (i.e. a cyclical process where technology destroys jobs but at the same time creates new ones). This time, the number of jobs that are being automated is higher than the ones created.
One of the possible solutions is to provide individuals with those skills that AI and machines cannot mirror, namely: creativity, collaboration, critical thinking, communication and empathy. These skills are prerogatives of disciplines called “humanities” that have been disregarded in the recent past in favour of engineering, computer science and math. Finally, these skills shall be supported by programming and coding competencies in order to enable individuals to directly control machines and software.
In other words, humanity will focus on those disciplines that put mankind at the centre of everything. As a matter of fact, to save human jobs we shall rediscover the skills that differentiate individuals from machines, and make human workers irreplaceable by any algorithm.
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