Are Robots Firing Us or Freeing Us?
Skynet. This is the first thought that crosses the mind of many opponents of artificial intelligence research. This fear of machines rising up and rebelling against humanity to turn us all into batteries is a legitimate fear of these critics. In October 2014 at MIT’s Centennial Symposium, Elon Musk, founder and CEO of Tesla Motor Company — a billion dollar company that produces all electric sports cars and experiments with self-driving vehicle technology, warned MIT students of the potential dangers of artificial intelligence and predicted it to be the “biggest existential threat” to mankind. Musk goes even further by comparing artificial intelligence to “summoning the demon” (AeroAstroMIT).
This “demon,” or what champions of artificial intelligence research celebrate as an “angel,” is simply a combination of advancing technologies such as machine learning, machine vision, and mobile robotics that leverage sophisticated algorithms to automate tasks and solve problems. While the point of this essay is not whether robots will turn humanity into batteries, illustrating what this technology is potentially capable of in the future will help readers to understand why this current technology is a part of a world wide, contentious debate. In this recent debate that include issues such as safety, privacy, and the aforementioned doomsday scenario, one of the most controversial issues currently affecting society is how artificial intelligence is impacting the future of employment, or unemployment, through automating jobs. While both critics and supporters of artificial intelligence have valid concerns that unemployment will negatively affect society, I urge both sides to consider a novel proposal that allowing robots to automate jobs will give humanity freedom.
Before making the case that robots will free humanity from employment rather than fire them from it, exactly how vulnerable are jobs to computerisation? This question is answered by a 2013 study by Carl Benedikt Frey and Michael A. Osborne, both Co-Directors for Oxford Martin Programme on Technology and Employment and experts in labour markets and artificial intelligence respectively, published by the Oxford Martin Programme on Technology and Employment. According to the authors, the study was motivated to answer the above question because of John Keynes’s, who is considered to be one of the most influential economists of the 20th century, prediction of burgeoning technological unemployment “due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour” (Keynes 3). Frey and Osborne tested how susceptible jobs are to computerization through a novel methodology of measuring advances in machine learning and mobile robotics, analyzing the automatable qualities of job related tasks, and cross referencing those qualities against the O*NET, a database containing over 702 standardized, detailed occupational descriptors. Using a Gaussian process classifier, Frey and Osborne were able to estimate the probability of computerisation for these 702 occupations (31).
According to their estimates, the results showed that 47 percent of US jobs are at risk of computerisation. The occupations most at risk include routine and low-skilled work. However, the study discovered that even jobs with non-routine, cognitive work like paralegals are also at risk for computerisation, pointing to recent software that was able to categorize thousands of case files overnight. Interestingly, the kind of work found to have the least risk is creative and social-skill intensive work like fashion designers and negotiators (Frey and Osborne 47). These findings have immediate implications for routine, low-skilled, under educated workers that will almost inevitably loose their jobs to a faster, cheaper, and more efficient computer. Frey and Osborne conclude that these workers will be either forced to develop their social and creative skills or become unemployed. However, these findings also have long term implications for the rest of the 47 percent of the US labor market who will eventually be forced to sharpen the same skills or become unemployed.
While Frey and Osborne may use the word “unemployed,” I champion for the term “freed.” I support this point by pointing to the “Equilibrium Unemployment Theory” by Christopher Pissarides, who is Professor of Economics at the London School of Economics, a research associate at the National Bureau of Economic Research, and the recipient of the 2010 Nobel Prize in Economics. Pissarides’ theory suggests that when labor markets destruct through disruption, such as technology, there is an equilibratory, capitalization effect in that market or industry that lead to new jobs which exploit the private gains from those industries (75). For example, when William Lee transformed the textile industry with his stocking frame knitting machine in 1589, most textile artisans lost their jobs knitting cotton. However, although the technology made the artisan obsolete, the gains that were capitalized for textile companies from the increased efficiency were distributed throughout the labor force in the form of increased pay for managerial work instead (Frey and Osborne 7). In this example, I argue that the artisan was not fired but freed from the hard, manual labor of knitting cotton and given lighter, higher paid work as a manager. This is proof of the “Equilibrium Unemployment Theory” that redistributes the wealth gained from increased profits in one area to another. When this theory is applied to Frey and Osborne’s research, it is easy to argue that the excess money realized from increased profits from 47 percent of the labor force being automated, or freed, will be redirected into creative and social endeavors. Endeavors that humans would arguably enjoy and be doing anyways. Imagine a world where humans are paid more to be creative and social than to analyze and categorize thousands of case files.
A world like this does exist. The country of Qatar is prime example of what can happen when a nation drastically increases it’s capitalization; they reinvest into the people. According to BBC News in 2013, Qatar used to be one of the poorest countries in the Persian Gulf while surrounded by oil rich nations such as Saudi Arabia. This was until the 1990s when Qatar discovered they were sitting on the single largest natural gas field in the world. Qatar is now the worlds richest country in terms of GDP per capita, double that of the United States. While Qatar has admittedly spent a large sum on opulent hotels and skyscrapers, the nation is also investing the gas wealth into its’ human capital by increasing wages 60 percent to state employees, providing land grants, and funding free university for citizens (Kinninmont). While I admit that these benefits did not happen specifically because of robots or computerisation, the crux of what happened in Qatar is a prime example of what can happen when there is a dramatic capitalization effect in a market, the population exploits the gains, pointed out by Pissarides. Robots automating 47 percent of jobs will capitalize the US as the natural gas fields capitalized Qatar.
While robots may still have the opportunity to turn us into batteries sometime in the future, for now, robots hold the key to the cell that keeps us trapped in nine to five. Robots are more than willing to volunteer themselves as the new work horses of the economy, while humanity enjoys the employment of freedom.
Works Cited
AeroAstroMIT. “One-on-one with Elon Musk.” Online video clip. YouTube. YouTube, 31 Oct. 2014. Web. 16 July 2016.
Frey, Carl Benedikt, and Michael A. Osborne. "The Future of Employment: How Susceptible are jobs to Computerisation." Oxford Martin School Programme on Technology and Employment. (2003): 1-72. Web. 21 July 2016.
Keynes, John. “Economic possibilities for our grandchildren”.
Essays in Persuasion. (1933): 3. Web. 21 July 2016.
Kinninmont, Jane. ”Qatar's delicate balancing act." BBC. BBC, 16 January 2013. Web. 20 July 2016.
Pissarides, Christopher. Equilibrium Unemployment Theory. Cambridge: MIT press, 2000. Print.