Introduction
The fear of unemployment due to automation is nothing new. In the 19th century, machines in the textile industry were destroyed by protesters (Thompson, 1977). Today, we are regularly warned about unemployment as a result of automation (Casper, 2016; Dijkstra, 2019). In the 1960s, President Lyndon B. Johnson set up a commission to investigate the supposed danger of automation (Autor, 2015). The commission concluded that while automation would replace some jobs, it would not eliminate work entirely (Bowen, 1966). However, many people believe that this time will be different. They expect that self-learning robots will soon lead to a lack of paid work for everyone. This expectation is based on two assumptions. The first is that our need for goods and services produced by human labor will grow more slowly than the increase in productivity from self-learning robots. The second assumption is that self-learning robots will be able to take over the jobs currently performed by humans. In the following paragraphs, I will provide arguments in favour of and against these assumptions. A conclusion is given in the last paragraph.
Why many of today’s jobs are vulnerable to automatization
According to estimates from CEDA (2015) and Schwab (2017), 35-50% of jobs in Western countries are vulnerable to automation. This is due in part to the increased computing power and falling costs of computers (Nordhaus, 2001), which has allowed for the development of self-learning robots that can perform tasks previously only possible for humans (CEDA, 2015). For example, robots may soon be able to pack different products by navigating in three-dimensional space and sensing the size and quantity of the products. However, the current limitations of robots, such as their limited computing power, may prevent them from fully replacing human workers in certain tasks. Moore’s law predicts that the computing power of computers will increase exponentially (Moore, 1965), which may enable robots to become more advanced in the near future. In addition, advances in networking technology have allowed for the centralized control of multiple robots, reducing their costs and increasing their effectiveness (CEDA, 2015). It is often assumed that humans and computers compete with each other in terms of absolute effectiveness. In practice, however, this is not the case. A robot that is ten times slower than a person but 100 times cheaper, is still profitable. In addition, a robot does not have to work flawlessly, it just has to be better than the person it replaces. These principles are important in valuing the competitive position of the robot compared to humans.
Will self learning robots replace paid labor?
In the previous paragraph three reasons are given why intelligent robots may take over people’s jobs. Yet there is no historical indication of a reduction in the total number of jobs as a result of automation. Labor participation has increased over the past hundred years despite technological developments. The demand for goods and services, has shifted, but has not fallen (Autor, 2015). Besides the fact that there are no indications of a reduction in the demand for goods and services despite increased productivity, the question remains whether intelligent robots will be able to replace humans in the labor market in the first place. There are many skills that people possess but we cannot describe how we perform those skills. An example of this is the human trait of empathy, almost all people possess empathy but we cannot accurately describe how we show empathy. Because we cannot accurately describe how we display this property, we can only teach these skills to machines to a limited extent.
Ways of self learning capacity
There are several ways in which self-learning capacity can be developed (Nilsson, 1983). A widely used technique is the ‘brute force’ method, in which a lot of data is used to allow the computer to make connections itself (Alpaydin, 2016). This is currently happening mainly with image recognition. The computer is presented with a large number of images and then asked to determine which images show a particular object. After the computer has made a selection in the images, the computer is shown which predictions are correct and which are incorrect. The computer then adjusts the algorithm to make a better prediction. This process is repeated until the computer arrives at a correct prediction. In practice, the computer often gives a good answer with this method, but also makes many mistakes because the computer cannot reason (EconTalk, 2020). For example, it turned out that a computer was able to distinguish chairs from other objects on the basis of the height and size of the seat. But the moment the image is disturbed (for example because someone sits on the chair), the computer can no longer determine that it is a chair (Murphy, 2000). The self-learning computer can therefore be an important tool to support professionals, but not a replacement because the computer cannot reason enough to independently fulfill a function. The technique of recognizing images is already successfully applied in recognizing a disease in X-rays. In certain cases, the self-learning robot is even more accurate than the human radiologist (Hosny, Parmar, Quackenbush, Schwartz, & Aerts, Hugo JWL, 2018). Nevertheless, the radiologist remains of great importance in interpreting the results of the analysis in the context of the patient.
In addition to jobs that consist of some degree of standardized work such as the aforementioned radiologist, there are also jobs that take place in a constantly changing environment. An example is the work of an organizational consultant. The consultant must weigh up different interests for each assignment in an environment that is highly subject to change. As a result, the work of the consultant cannot be translated into a collection of rules (if x then y). If these rules cannot be created by a programmer or by a self-learning algorithm, the work cannot be taken over by an intelligent robot.
Are self-learning robots harmful for society?
All in all, it can be said that the self-learning robot will not take over all paid work, but probably a significant part of it. The question that remains is, to what extent is this development harmful? This question can best be answered by looking at the effects of self-learning robots entering the labor market. In addition to positive effects such as cheaper products and a smarter healthcare sector, there are a number of effects that can have negative consequences for a large group of people. An effect that can already be observed is the effect of orbital polarization. The self-learning robot currently mainly affects jobs in the middle segment of the labor market. Here, routine activities are carried out that can easily be taken over by intelligent technology. An example of this is the position of shop or counter clerk. These functions are increasingly being taken over by online services and machines such as ATMs and collection machines. In addition, administrative tasks have been further automated or outsourced to the customer, so that fewer employees are needed. Employees who cannot work their way up to the top of the labor market are pushed to the bottom of the labor market. The demand for labor has increased faster at the top of the labor market than at the bottom, widening the gap between high and low incomes (Autor, 2015). In order to deal with this problem, training opportunities are important to realize sufficient growth opportunities, even with a shrinking middle segment. Learning analytical and interactive skills are particularly important here because these skills are complementary to intelligent automation systems (CPB, 2015). In addition to job polarization, there is a second way in which the self-learning robot promotes inequality. The owner of the robot is the only one who earns from the productivity increase that the robot brings. The employee who is more productive or in the worst case replaced by the robot does not share in the profit. As a result, the return on capital is significantly greater than the return on labor. Which further increases inequality. A possible solution would be for companies to be obliged to provide some form of profit sharing or payment in shares to their employees. This allows both investors and employees to benefit from increased productivity (Went, Kremer, & Knottnerus, 2015). In addition to the fact that automation affects the segments of the labor market in different ways, it also influences the sectors of the labor market in different ways. Productivity gains due to automation are smaller in the services and public sectors, because services are more difficult to automate than manufacturing products. This makes services relatively expensive compared to products. In addition, the demand for services is more flexible than the demand for products. As prosperity has increased, people have started to spend a larger part of their income on services. Because the public sector mainly consists of services, it will form an increasingly larger share of the gross national product. As a result, economic growth levels off and the public sector becomes unaffordable (Baumol & Bowen, 1966). A solution to the problem would be further privatization of the public sector. But as with the aforementioned effects, this will increase inequality because not everyone will be able to afford the same level of services.
Conclusion
It is likely that the self-learning robot will have a major impact on the labor market as we know it today. The extent to which this impact will be harmful depends on how we will deal with the entry of the self-learning robot into the labor market. The biggest challenge will be to set up the system in such a way that this new technology not only serves the interests of the owner, but also of the employees who will become unemployed due to the arrival of the self-learning robot. In addition, it must be prevented that the costs of the public sector become so great that economic growth is curbed.
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