The Rise of Automation and its Relationship to Technical Unemployment
Increasingly, a number of recent technological advances in engineering and computer science have allowed for breakthroughs in the complexity of tasks automated workers and programs can handle, as well as a decrease in their pricing.
Technical unemployment refers to this trend of job loss as a direct result of technological breakthroughs that increase the efficiency of the average worker many times. One glaring example of these advances manifests in Tesla’s self-driving technologies that has already seen implementations in its more recent electric car models.
Tesla’s self-driving technology, coupled with additional refinements and advancements from its competitors will eventually create a more robust self-driving software capable of threatening taxi markets or even markets of more popular startups such as Uber or Lyft, assuming that they do not already have plans to account for such technologies.
As a metaphor, if people were digging tunnels with spoons before and an industrial tool was invented, the increase in efficiency from industrial tools such as drills would lead to a massive trend of unemployment for those using spoons because the drill would do the work of many people at a cheaper cost.
The technological revolutions in automation are comparable to the invention of industrial tools, except they apply to the market on a much greater scale and in multiple sectors such as in accounting, sorting materials, driving, manufacturing and so forth.
As technology improves, jobs previously requiring manual oversight and human power will become scarcer, leading to a decline in the demand for human work in those sectors.
Scholars must understand the implications of such a revolution and prepare for the consequences of far-reaching technical unemployment.
The concern over computational advances in leading to automation of common tasks threatening job markets is not a new one, and recent studies show that:
47 percent of U.S. employment stands at risk due to computerization (Marchant, Stevens, and Hennessy 28).
These authors have raised a serious issue and their publication in the peer-reviewed Journal of Evolution and Technology highlights the relevance and expertise of their opinions. The greatest issues here are the fact that the risk to employment is tangible and highly likely in the future, and that such an expectation of risk implies that there will be a large portion of the American population displaced. The definition of technical unemployment is recognized by a number of authors, and formalized as:
“…a growing concern that emerging technologies such as computers, robotics, and artificial intelligence are displacing human jobs… if true it will have major economic and social repercussions for the future” (Marchant et al. 26).
The clear issue is that there appears to be an inevitable time where automation of certain technologies will occur, and that there are not adequate social and economic policies in place by the government to control for the scale of such a massive technical unemployment once those technologies become useable and widespread.
We first consider a number of existing, historical industries under direct threat of massive economic and social upheavals following the maturation of automation to show the context of the problem.
Afterwards, the discussion will move towards possible counterpoints and possible policies that may be undertaken to account for the arrival of automation and technical unemployment.
In order to better capture the gravity of this problem, consider the field of trucking as well as driving services as a whole and the risk of technical employment in the near future to that industry.
Trucking is a massive industry in the United States, employing over 1 million individuals and generating $7 billion in revenue.
The Bureau of Labor Statistics reported in 2015 that there were around 1.7 million heavy and tractor-trailer truck drivers in the US with a median pay of around $40,000 (BLS.gov).
While the argument that automation will replace tasks requiring basic human decision making and direction seems abstract at first, this notion of automation being “further away” or in the distant future is immediately refuted by the fact that self-driving software are already available and being tested and refined by major companies.
For instance, a company called Otto has already completed their initial trials of automated truck driving by successfully sending a freight truck of 1,000 cases of Budweiser over 120 miles without driver guidance.
But as of now, Tesla has already broken that benchmark and limitation as we will see later below.
Moreover, trucking is not the only field with its jobs under threat from automation, the entire driving services industry as a whole may be threatened. In the past, consider that Tesla has made frequent public announcements and even advertises a self-driving feature in its models, boldly advocating that:
“All Tesla vehicles produced in our factory, including Model 3, have the hardware needed for full self-driving capability at a safety level greater than that of a human driver” (Tesla.com).
In on 2016 piece on the concept of artificial intelligence, The Economist makes several significant observations regarding the position of the driving services industry. The article begins by stating that advanced technologies that draw on deep learning algorithms to use X-ray and CT scan data have already found healthcare applications, showing the extent of how developed artificial technologies and automation can become. The article makes the fascinating point that:
“What determines vulnerability to automation, experts say, is not so much whether the work concerned is manual or white-collar but whether or not it is routine. Machines can already do many forms of routine manual labour, and are now able to perform some routine cognitive tasks too” (The Economist 2016).
Therefore, the routine nature of truck driving and ferrying services such as those offered by taxis may be counted as vulnerable to or at risk for technical unemployment due to the routine nature of the task of driving.
One might question the inevitability of such advances in automation leading to widespread unemployment, but this notion is less unrealistic if we consider that machines do not need to be paid, given vacations, breaks, food, or other similar accommodations and that some automated features of machines can reliably outperform the average human worker as well.
In comparison to a human worker that cannot work 24/7, does not provide consistently reliable results on average, requires oversight, and that presents a potential liability in how they may be injured or how they may unionize for greater pay and rights, a robust machine capable of handling basic routine intellectual tasks will be infinitely preferable to companies if they seek to remain competitive.
Again, a machine can provide many benefits to a company in addition to out working the average employee. Even assuming competition from the best humans putting in 12-hour shifts, a single robot might replace two workers, and possibly as many as four (Larson 2012). Of course, machines do not get sick either or take breaks in contrast to their human counterparts.
Even discounting the fact that machines may perform at an equal or higher level continuously than humans, this represents a huge boost in productivity.
In an objective, peer-reviewed study published by the National Institute of Health, Rebecca Mitchell and Paul Bates give an approximation in the overall productivity losses due to worker health. They surmise that:
“Decreased on-the-job productivity and employee absence because of health result in significant costs to employers above and beyond medical spending. Health related work losses are estimated to cost US employers more than $260 billion each year, and may cost some companies more than direct medical expenditures” (Mitchell and Bates 93).
While it is important to recognize that this figure of $260 billion represents the entire U.S. market rather than only the industries most likely affected by technical unemployment due to automation, it still highlights the staggering financial losses companies suffer in compensating for employee health: an issue that is arguably irrelevant to an automated labor force of machines.
Furthermore, another issue that makes human workers much less appealing to companies are the presence of unions and the legal role that unions play in negotiating higher wages and better working conditions for their workers and constituents. These negotiations obviously produce a loss for the company as they are forced to bargain for labor and provide their employees with a number of benefits.
Consider the case of unionization efforts against Hostess and their flagship product Twinkies, which almost led to the ruin of the brand. Forbes journalist Tim Worstall documents these episodes in a report, noting that the shedding of unionization and embracing of an approach utilizing automation helped rescue the business from bankruptcy:
“The most recent investors who bought it out of bankruptcy did not in fact buy ‘the company.’ They bought just some of the assets. This meant they could dump that entire union-based labor negotiation system…” (Worstall 2016).
Worstall further documents that the subsequent firing of 95% of the previous workforce and efforts made to streamline the remaining assets into a model that embraced automation eventually made the brand profitable again. In this case, it is obvious that improperly handling unionization creates serious financial issues for a company.
Further, automation served as a direct method of dealing with the pressure from unions and even led to the success and revival of a brand like Twinkies through technical unemployment. When more reliable and complex automation technologies become available, it is a very real possibility that more companies will favor firing a majority of their routine workers in favor of machines to lower the costs of production and legal risks to their assets.
Significantly, if a massive technical unemployment across American industries with jobs that involve a high level of routine will coincide with the advancement and accessibility to automation technology is inevitable, what are the appropriate policies to implement in order to account for the social and economic upheaval that such automation will bring? John Danaher gives a positive perspective on the idea of increasing automation with regards to it might impact our future standard of living.
He gives a basis for conceptualizing how automation positively benefits personal fulfillment if we choose to accept the social and economic change it will bring: “…this threat could be contained if we adopt an integrative approach to our relationship with technology” (Danaher 41).
Danaher’s approach takes many different considerations such as whether a technological (technical) unemployment that shakes the social and economic underpinnings of the United States is even plausible in the near future, we will consider opposing perspectives after exploring the assumption that such technical employment will inevitably occur. The key philosophical point that Danaher mentions before is integration with the technology.
The danger that Danaher identifies stems from the idea of an “antiwork critique” which essentially claims that the advent of automation will rob our lives of meaning by reducing the amount of work necessarily to obtain certain results in society. He writes that “to mitigate these concerns” we should “pursue increased integration with technology, not increased externalism” and remarks that how meaning is conceptualized post-automation depends how we choose to define it in relation to that technology (Danaher 43).
How do we define a successful integration with robust automation technology? Briefly, from Danaher’s perspective, he claims that integration involves considering a number of perspectives against the anti-work critiques in making a case for optimism after mass technical unemployment and a more relatable “subjectivist argument”.
The subjectivist argument he outlines is that “compulsory work takes us away from things that we are really passionate about and that would confer upon us the most subjective satisfaction (Danaher 53). In other words, while eliminating a massive number of routine jobs may initially cause instability with regards to a traditional social and economic order, if our society is capable of understanding automation as aiding us through allowing us time to actualize our passions we may still be able to find fulfillment in our life through other means.
However, this optimistic viewpoint assumes a number of practical requirements. And that future is already here with the advent of machine learning methods along that have allowed for Tesla’s feasible self-driving trucks along with engines such as a AlphaZero which are able to beat even the reigning computer programs which have been specialized and refined over years to play chess.
But in the end, who knows which direction the widespread usage of that technology will take us?