STUDENT ENTRY

Student Entry Name - Edwin Rodriguez

Submission Date - May 10. 2019

Topic Title - "Technological Unemployment"

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Technological Unemployment is the term used to describe human job loss due to automation and/or machine learning. The term has seen recent popularity due to the rise of public interest in artificial intelligence and society’s vast technological advancements. To understand the term technological unemployment and its relevance in society, we must look into the events that led up to the creation of this term and the affects.

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The industrial revolution brought forth the first instances of automation which increased production and cut costs for manufacturing and agriculture. This old automation consisted of clunky machines that were cost effective in narrow situations for what they were built for. For example, assembly line machines, the cotton gin, and the tractor, all cut the biggest expense to a company’s productivity, human labor. This old automation replaced human muscles with “mechanical muscles” because mechanical muscles are stronger, more reliable and tireless than their human counterparts (Grey, 2014). This shift in preference to mechanical muscles was not only seen in human labor, but horse labor as well. As the first cars, both industrial and personal, came into the scene the need for horses dropped. No longer were horses used on ranches, used for transportation or brought onto the battlefield. Because the need for horses was no longer there, the number of uses for horses dropped as well. The automation of the past contributed to the growth of the economy and rise of the standard of living for both humans and horses. The automation that is being discussed in technological unemployment is slightly different thanks to artificial intelligence and machine learning.

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Technological development in society isn’t always about the new expensive tech of the modern era, but usually decade old tech being more widely available and optimized. A good example this is the car. Cars foresaw the end of horse labor and are now also foreseeing the end of human mind labor. Just as mechanical muscles pushed horses and a portion of blue-collar workers out of the American economy, mechanical minds will do the same to human minds (Grey, 2014). Google and Tesla are developing self-driving automobiles to remove the need for human drivers, who are prone to mistakes and mishaps. In 2017, there were 40,000 deaths in the U.S. caused by automobile accidents (Bomey, 2017). The autonomous systems within self-driving cars eliminate human error which in turn will cause less accidents on the road. Not only are autonomous systems being implemented in cars, but they are being implemented in other job functions with the same goal in mind, to cut costs at optimize the end result of the job function. Many supermarkets have now implemented self-checkout lanes, and some fast food services have touch-screen ordering kiosks. These businesses are replacing jobs that were once performed by a human and consisted of human interaction, with machines. Humans cost minimum wage as well as liability and benefit expenses. Economically speaking, an automated machine is more cost effective compared to a human. In the case of the supermarkets, it is cost effective to have one cashier supervise 6 autonomous self-checkout lanes, rather than have 6 normal registers being maned by 6 employees. About 35% of current jobs are at high risk of being automated over the following 20 years, according to a study by researchers at Oxford University and Deloitte (Stylianou, 2015). The jobs that are at risk of automation are no longer just blue-collar jobs, white collar jobs are up for grabs as well thanks to machine learning. The floor of the New York Stock Exchange used to be filled with day traders, now its all just tv sets and computers displaying information. That’s because the NYSE is just automated bots trading with other automated bots. If your job can be easily explained, it can be automated (Lepore, 2019).

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