Automation. Are you safe?

Toyota and Pizza Hut recently announced they are teaming up to develop an autonomous pizza delivery vehicle. Almost as if inspired by Charlie Brooker himself, the news is just another reminder of our movement toward an inevitable technological flourishing. Whether that’s for better or worse has yet to be decided. Yet, every time such an announcement is made the conversation of automation and job loss circulates the mainstream discourse. While there is justified concern over the displacement of jobs, very few question what is the optimal outcome in the wake of an automated world. If the synergy of automation and advanced AI into capitalism is successful, what newfound difficulties will we face? It is now, as we stand on the precipice of this advancement that we must begin asking such questions. For if we are to even attempt to form some sort of contingency plan, it must be done now before we are too far gone. Over the next few weeks, I will be compiling a small series about these topics and addressing some of the issues that we as humans will inevitably face.

Over time we have seen major innovations ease human labour, and increase productivity within the workforce. From this more goods could be produced, menial jobs were eliminated, and higher skilled roles were created. It is because of this that standards of living increased, and more jobs were offered to a growing population. During the industrial revolution, this shift in work occurred through the creation of production jobs. However, many of these were replaced with the rise of basic automation during the 21st century, and so people moved on to service work.

Often it can be difficult to grasp the sheer impact automation will have on the world. Many assume those at risk are a minority, and in due time employment levels will rise once again. Nevertheless, concerns are warranted not just for those in low-skill work, but possibly for many of the workforce today. Such worries of mass unemployment were never more prevalent than during the Great Depression. During a time of economic uncertainty, unemployment reached a historical maximum of 22.9% in 1932 (Tapia Granados & Diez Roux, 2009). This figure pales in comparison to the 47% of employment in the US is currently at high risk of being automated in the coming two decades; more than double the level of unemployment seen during the Great Depression. These jobs are those of transportation workers, administrative and production workers. Service occupations, which have seen the most growth in the past few decades – and which were a direct response to the increased automation during the 21st century – are at a worryingly high risk of being automated. Yet this growth will be insignificant when fast food cashiers are replaced by touchscreens. While its unknown the rate at which automation adoption will occur in these industries, it's important to understand that so long as companies prioritise profit maximisation, the change is an inevitability. Even former CEO of McDonalds, Ed Rensi admits “It’s cheaper to buy a $35,000 robotic arm than it is to hire an employee who’s insufficient making $15 an hour bagging French fries,” (Taylor, 2016). Because of the epidemic levels of automation that is going to occur, unemployment will run rampant across global industries.

It may be consoling to assume that this will only lead to temporary unemployment, and with the right political and economic forces, all will be corrected for. Technological unemployment during the Industrial Revolution saw many concerns that workers would become redundant with the rise of machinery. Worries were soon quelled as the mechanization was only able to replace limited human activities, and drove demand for new forms of labour that were inadvertently created (Mokyr, Vickers, & Ziebarth, 2015).

Many assume job creation similar to that of the industrial revolution will occur. However, this time is different. Firstly, when looking at that 47% of jobs at high risk, there is a strong negative relationship between wage and educational attainment (Frey & Osborne, 2013). Given the high risk, workers will have no choice but to seek work that is harder to be automated. Most of these jobs, however, involve the completion of a tertiary education. When looking at the current educational climate in America, the likelihood of these low-wage households attaining such an education is consistently decreasing due to an ever-expanding class divide. The likelihood of students completing a BA or BS by the age of 24 has only increased for those in the upper economic half of America (Berg, 2016), those least at risk of job automation. Moreover, because of the rigid structure of American education, those from lower-income households struggle to adapt to a college education due to personal, family or economic problems in their lives. This often leads to them dropping out, having difficulties learning or simply not trying as hard as they need to. Not only will these households lose work from automation, but the opportunity to find secure work is quickly becoming an impossibility.

The structural rigidity of education in America is only furthered by the prevalent class divide. A divide which is the greatest it has been since 1928 (Lacy, 2015). A year that is worth noting, came right before the period that saw America’s unemployment rate at its highest, The Great Depression. It is not just America that sees this divide, however.  In 2016, the global wealth owned by the top 1% of the population surpassed 50%. Just last year in 2017, that 1% now controls 82% of global wealth (Oxfam International, 2018). Even if one were to accept the reality that many low-wage jobs will be eradicated through automation – given the current economic climate of the world – it is going to become increasingly difficult for unemployed individuals to acquire the skills necessary to combat to growing risk of automation. Even if they are to acquire such skills, the jobs that they are able to attain are still at great risk of being rendered useless by automation.

Through machine learning – the process through which AI automatically learns and improves from experience without explicit programming – jobs that would otherwise seem safe from automation, are perhaps at more risk than those in factories. Certain AI is being designed to identify processes within businesses that can be easily automated, essentially cutting out middle management from firms. Any human tasks that need to be completed can then outsourced to freelancers online. Doing so cuts cost by 50% for the business within the first year, and by another 25% in the second (Kurzgesagt, 2017). Martin Ford (2016), in his book Rise of the Robots, describes how jobs that are considered highly skilled are now at risk of automation. The example he gives is of Radiologists, who are trained to review the images of medical scans. However, as Ford highlights, image recognition technology is advancing so rapidly that it will soon render the traditional role of radiologists useless. It’s not just radiologists. The likelihood of highly skilled work being automated is ever increasing as technology advances.

During the Industrial Revolution machines were able to compensate for the lack of human strength and dexterity. Unlike today, those robots weren’t able to think, sense, perceive or analyse. Current technological trends are exponential, and see an ever-increasing growth in the capabilities and intelligence of robots. this is best encapsulated by this quote by Martin Ford.

Imagine that you get in your car and begin driving at 5 miles per hour. You drive for a minute, accelerate to double your speed to 10 mph, drive for another minute, double your speed again, and so on. The really remarkable thing is not simply the fact of the doubling but the amount of ground you cover after the process has gone on for a while. In the first minute, you would travel about 440 feet. In the third minute at 20 mph, you’d cover 1,760 feet. In the fifth minute, speeding along at 80 mph, you would go well over a mile. To complete the sixth minute, you’d need a faster car—as well as a racetrack. Now think about how fast you would be traveling—and how much progress you would make in that final minute—if you doubled your speed twenty-seven times. That’s roughly the number of times computing power has doubled since the invention of the integrated circuit in 1958. The revolution now under way is happening not just because of the acceleration itself but because that acceleration has been going on for so long that the amount of progress we can now expect in any given year is potentially mind-boggling. The answer to the question about your speed in the car, by the way, is 671 million miles per hour. In that final, twenty-eighth minute, you would travel more than 11 million miles. Five minutes or so at that speed would get you to Mars. That, in a nutshell, is where information technology stands today, relative to when the first primitive integrated circuits started plodding along in the late 1950s.
— Martin Ford, 2016

This time is different. We run the risk of seeing mass amounts of job displacement due to technological changes. It is now that we must take precautions to combat what is currently a growing inevitability toward an unemployment crisis. If we can succeed at doing this, we may see a time of true flourishing among humanity unlike anything seen before. A time in which work is minimal, leisure is abundant and economic concerns are non-existent. While this is no doubt a Utopian dream, it’s a reality that is not far-fetched. We have to make the proper decisions today. If we do, perhaps a life of leisure of true human flourishing awaits us.

 Next we will address some of the processes that could lead to such a utopia dream, and what opportunities could away humanity if we suceed. 

 

Works Cited

Berg, G. A. (2016). Low-Income Students and the Perpetuation of Inequality. New York: Routledge.

Ford, M. (2016). Rise of the Robots. New York: Basic Books.

Frey, C. B., & Osborne, M. A. (2013). The Future of Employment: How Susceptible are Jobs to Computerisation? Oxford: Oxford Martin School.

Kurzgesagt. (2017, June 8). The Rise of the Machines - Why Automation is Different this Time. Retrieved from Youtube: https://www.youtube.com/watch?v=WSKi8HfcxEk

Lacy, K. (2015). Race, privilege and the growing class divide. Ethnic and Racial Studies, 1246-1249.

Mokyr, J., Vickers, C., & Ziebarth, N. L. (2015). The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different? Journal of Economic Perspectives, 29(3), 31-50.

Oxfam International. (2018, January 22). Richest 1 percent bagged 82 percent of wealth created last year - poorest half of humanity got nothing. Retrieved from Oxfam: https://www.oxfam.org/en/pressroom/pressreleases/2018-01-22/richest-1-percent-bagged-82-percent-wealth-created-last-year

Tapia Granados, J. A., & Diez Roux, A. V. (2009). Life and death during the Great Depression. Ann Arbor: University of Michigan.

Taylor, K. (2016, May 31). McDonald's CEO reveals how the fast-food chain will use robots in the future. Retrieved from Business Insider Australia: https://www.businessinsider.com.au/mcdonalds-wont-swap-workers-with-robots-2016-5