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Automation, Artificial Intelligence, and Inequality

Updated on December 18, 2016

Intro

What are the consequences of innovations in technology? Shaping economies and social orders, it is clear that technology plays a large role in the development of society. Now more than ever, technology is an underlying factor that is inextricably linked to civilization as a whole. However, it is important to consider what this means as technology develops. While the inherent goal of technology is to reduce manual labour and facilitate greater efficiency and production, it seems that the many virtues of technology may also bring forth immense challenges. As technology becomes more advanced it becomes increasingly difficult to see where humans fit into the picture.

In particular, the developing technologies of automation and artificial intelligence create a scenario in which human work becomes all but obsolete. Fully automated car factories and the Google self-driving car offer us a brief glimpse into what the future may hold. While these systems are currently only designed to operate in very specific and controlled scenarios, we are not far from a future where machines can learn and think for themselves. In this scenario it is likely that humans will act as mere facilitators to the work being done mechanically for them. However, a problem arises when we consider the transition between our current workforce and this possible future. While automation and artificial intelligence will be highly beneficial to society as a whole in the long run, the progression from our current structure to a fully automated workforce will not be instantaneous are smoothly accomplished. In reality the conversion will likely occur in a discontinuous and halting manner. Some jobs will be easily replaced, while others will be more difficult to automate effectively. This means that, in the transitory phase of technological development, a large portion of the workforce will be distinctly disadvantaged.

While many people will still retain their jobs, those who can be easily replaced by robots, will be left without a means of supporting themselves financially. The unequal development of technology means that a large portion of the population will be rendered effectively unemployable. Until machines have progressed to the point where they can replace service and professional jobs, it will likely be workers in the primary and secondary employment, (or resource and manufacturing), sectors who will bear the brunt of this consequence. In addition to this, developing countries, where manufacturing and agriculture constitute a larger percentage of employment, will also suffer disproportionately when compared to more service oriented developed countries. While a gap already exists between the upper and lower socioeconomic classes, and a similar financial gap also exists between developed and developing countries, it appears that improvements in technology may lead to an increase in this disparity. The unfortunate situation is that those who are already financially disadvantaged will suffer the most as a result of increased automation and the growing use of artificial intelligence.

Automation and artificial technology provide a promising future; however, we have to take into account the problems that this development may create. Examining the context in which automation and artificial intelligence are developing, we can see that they may lead to an increasing inequality on both a national and global scale. It is important that we fully understand the extent of this issue, in order to develop possible solutions. As this possible future approaches it is necessary that we consider how automation and artificial intelligence might exacerbate the division between classes and nations, as well as how we might prevent and mitigate this problem.

What's the Situation?

In order to facilitate a complex and nuanced discussion of the problems related to automation and artificial intelligence, it is vital that we have an understanding of the current state of inequality, on both a national and global level, as well as the current and projected capabilities of artificial intelligence and automation. We will then be able to more comprehensively determine the connection that exists between these situations. With that knowledge in hand, it will be a small extension to discuss the underlying problems inherent in both current and predicted circumstances.

I) Inequality

Inequality in and of itself is a major global issue in the world today. When we take a closer look at how inequality exists, it becomes clear that a gap occurs both between classes in countries, as well as between nations themselves. These simultaneous discrepancies are effectively represented by a graph that appears in Ortiz and Cummins’s paper Global Inequality: Beyond the Bottom Line.


The disparity between upper and lower classes within countries, is a concerning reality. While many modern developed countries operate under the ideals of equal opportunity for all people, it quickly becomes apparent that this is not the case. Looking at current trends in income and employment, it is evident that the state of class division is quite staggering. For example, in 2000, the richest 5% of all households in the United States made more than six times the income of the poorest 20% of households (Yates 331). Additionally, in 2010 the richest 400 households in the United States, (or the richest 0.003% of the population), were the benefactors of 16% of all capital gains in that year (O’Brien). These examples serve to illustrate the clear divide that exists between classes in developed countries. Furthermore, it appears that this problem is only getting worse. It is estimated that as much as 70% of all income growth in the United States goes to the richest 1% of families (Yates 331). Further aggravating this problem, it is extremely difficult to move between these established classes. If you are born into the top 20% of incomes, then you have a 42.3% chance of staying there, but only a 6.3% chance of falling into the bottom 20% (Yates 332). Contrastingly, those in the bottom 20% have only a 7.3% chance of rising to the top 20% (Yates 332). If we extend this idea, having parents in the top 1% of the income distribution means that your chances of ending up in the top 20% would almost certainly be greater than the 42.3% sited earlier (Yates 332). This self-perpetuating cycle has lead to a situation in which the higher classes posses a distinct advantage over the poorer classes. With the gap between classes continuing to widen, the richer minority is placed more and more firmly above the poorer majority.

Beyond the gap in developed countries, developing countries also experience a similar, if not more severe, level of class division. While many sources claim that “third-world” countries are growing economically by citing GDP and economic growth, this alleged development is undermined by the fact that, in China and India, “most of the benefits of rapid economic growth are going to the wealthiest 20% of society” (Yates 334). The apparent progress of developing countries as a whole misrepresents the actual state of inequality that exists. Economic development does not necessarily beget increased equality. As a result of their growth, developing countries appear to be approaching situations not unlike those of developed countries. In both cases we are beginning to see the potential threat of a developing oligarchy, where a small group, or upper class, control the majority of wealth and power in a society. The presence of firmly divided classes within nations is not only a reality; it is a reality that is getting worse.


In parallel to the idea of class disparity is the inequality that exists between nations on a global scale. In a worldwide perspective developed countries can be viewed as an upper class, while developing countries are representative of the lower class. The difference between rich and poor countries is just as distinct as the differences between rich and poor classes. As illustrates by the graph below, in 2007 the richest fifth of the global population made 63.6% of total income, while the bottom three fifths earned only 19% (Ortiz and Cummins 9).


In addition to this it should be noted that “Countries such as the United States… have per capital GDPs 20 to more than 100 times greater than countries like Ethiopia” (Yates 335). Despite the economic growth that is occurring in some developing countries, the gap that already exists between developed and developing countries means that it is unlikely that prosperity, (as measured by quality of life, GDP, life expectancy, etc.), in both will ever converge. If developing countries were to maintain their current pace of economic growth it would take more than eight centuries for the bottom billion to have ten percent of global income (Ortiz and Cummins 10). Overall, we can see that on a global stage developed countries, much like the upper class, hold a position of economic eminence over developing countries. While this situation is not necessarily getting worse, it is also not getting better at a significant rate. The global economy seems to be strictly divided between developed and developing countries, with little prospect of convergence between the two.

Inequality is an important problem that exists on both a national and global scale. On a national scale, this problem only appears to be worsening, while on a global scale there is a definite lack of notable improvement or progress towards increased equality. These conditions have the potential to be dramatically exacerbated by an increased use of automation and artificial intelligence.

II) Automation and Artificial Intelligence

Automation and artificial intelligence are currently among the leading areas of technological innovation. It has been predicted that as they develop, automated robots utilising artificial intelligence will progressively make up an increasingly large percentage of the workforce. As technology develops, machines will be endowed with an increased capacity to operate under diverse and changing conditions. Machines that can operate outside of specific prescribed conditions may gradually replace humans in progressively more complex jobs.

As a starting point, we should look at current trends in automation. Machine automation is “a major means for gaining and sustaining productivity advantages”, meaning that machines increase efficiency, both in terms of production and growth (Terweisch and Ganz 127). By minimizing the number of humans required, and thus the potential for error, automated machines increase reliability and lower the overall cost of production. Currently, automation is trending towards greater integration and optimization (Terweisch and Ganz 130-140). Specifically, increased automation leads to a greater integration of processes and communication. Because machines are much more reliable in their timing, processes can be scheduled in a very tight and controlled manner. An increasingly low rate of variability in the time that it takes for tasks to be completed means that, procedures can be scheduled so as to minimize waste of materials and time. Additionally, automated systems are much more effective at communicating issues, and addressing them as is necessary. When machines are programmed to know exactly how to report and respond to an issue, it removes a level of human involvement. Rather than relying on humans to deduce the cause of mechanical errors, automated systems increasingly incorporate a level of communication that allows the system to address its own problems. Ostensibly, this removes the need for human maintenance or repairs, so long as the machines automated system can effectively identify and repair any issues. Looking at these trends, we can see that automation is moving towards a level of complexity wherein automated systems will allow the optimization of almost any situation in which they are employed (Terweisch and Ganz 138). Overall, automated systems are moving towards greater efficacy and refinement, replacing many tasks that would normally be delegated to factory workers or managers.

Building on the productivity provided by automation is the technology of artificial intelligence. Artificial intelligence allows for complete machine autonomy, with little to no interaction from humans. While artificial intelligence currently allows for machines to deduce correct courses of action within a small scope of possibilities, trends in artificial intelligence predict machines that will have the capacity to think, learn, and solve problems for themselves. By considering the idea that “The essence of mental development is to enable robots to autonomously “live” in the world and to become smart on their own…” we can see how artificial intelligence will be a valuable tool as we attempt to mechanize more complex and multi-faceted tasks (Weng et al.). Rather than a programmer preinstalling designated responses for anticipated situations, we will be able to create systems that “…learn in real time while performing on the fly” (Weng et al.). In a situation where machines can teach themselves a task without specific programming, human involvement will be limited to the initial programming of a “developmental program”. By its very nature this program is extremely generic and allows robots “to automatically generate representations for unknown knowledge and skills” (Weng et al.). One such program could be used to teach many different robots a wide range of tasks. More advanced and widespread artificial intelligence could reduce the number of humans involved in a system to effectively zero.

Advances in technology are poised to radically change the landscape of our workforce. As automation and artificial intelligence become more complex, the number of humans required to complete a given task effectively will dramatically decrease, eventually approaching the point of nonexistence. This means that the easiest jobs for machines to learn, those that are inherently procedural, methodical, and with limited deviation, will likely be almost entirely replaced by mechanical labourers in the near future.

III) Overview

In summary, inequality and advances in technology are both major determining factors in shaping the future of society in many capacities. Both have an impact on relationships within countries, as well as between nations on a global scale. If we compare the situation in both of these cases, it is possible to make a connection between the two. The already existing inequality between classes and nations leaves economies vulnerable to change that may exacerbate the problem. Unfortunately, while they provide immense economic advantages, automation and artificial intelligence are poised to exacerbate inequality on multiple levels.

What's the Problem?

On a national level automation and artificial intelligence are likely to have an impact on the division between the wealthier and poorer economic classes. However, this division may not be a clear-cut situation where the rich get richer and the poor get poorer. Instead it appears that certain skills and employment situations are likely to prove more advantageous when compared to others. Specifically, the already existing situation of socioeconomic classes, and the power of technologic literacy may play an important role in determining who is successful in a more technology-driven economy. Current trends in manufacturing provide a glimpse into the possible future of employment.

The manufacturing industry seems to be facing the greatest number of threats from automation and artificial intelligence. By examining trends that are currently occurring in this employment sector, we can see the effect that automation and artificial intelligence may have on many others jobs and types of employment. Because manufacturing can, in most cases, already be easily automated with technology, it is currently the most vulnerable economic sector. By looking at trends in manufacturing employment, we can get a better understanding of the challenges that will eventually face other jobs. Following the industrial revolution manufacturing made up the largest portion of jobs within developed countries, such as Canada and the United States. In fact, manufacturing was the largest economic sector in Canada from the 1820s-1940s (Bernard 7). Since then, manufacturing has taken a back seat to the tertiary, or service, economic sector. In recent years there has been a drastic decline in employment in the manufacturing sector among developed countries. Specifically, following the turn of the century manufacturing employment has decreased dramatically. Between 2004 and 2008 one in seven, or 322,000, manufacturing jobs were lost in Canada alone (Bernard 11). These loses have been largely attributed to economic turbulence and outsourcing to foreign countries, but one can extend these current trend to incorporate future automation and artificial intelligence. In the future, it will become more common for jobs to be outsourced, not to other countries, but to machines. Even now, in the early stages of automation and artificial intelligence, we can see the effects that technology is having. When we consider that “production generally decreased less than employment, meaning that some of the job losses can be attributed to increased productivity in manufacturing industries” it becomes evident that while production was decreasing, “businesses were also becoming more efficient and could produce more with the same workforce” (Bernard 11). This is almost certainly a result of increased efficiency as facilitated by increased automation and artificial intelligence. We can extrapolate that the problems currently facing the manufacturing industry will probably occur similarly in other economic sectors, as automation and artificial intelligence become capable of performing more complex tasks. Automation and technology reduce the amount of workers required to perform the same task, while also operating at much greater efficiency

With automation and artificial intelligence limiting the number of available jobs, the future employment market will likely be one that is highly selective and discriminative in terms of who will be hired. Only the most effective workers will be employed in an extremely restricted number of jobs. This trend is likely to lead to a “hyper meritocracy”, where in a small number of specifically advantaged workers hold jobs, while the rest of the workforce is left unemployed and obsolete (Cowen 38). Specifically, the problem is that automation and artificial technology leave little space for human involvement, thus only a small number of humans will have the requisite skills to remain meaningfully employed. By the virtue of their high skills, these advantaged people will be placed above those who do not facilitate the greatest level of efficiency when working in collaboration with machines. Automation and artificial intelligence may promote changes that will lead to a diminishing middle class and an extreme polarization of wealth. This problem lies most directly in correlation with the discerning factors of who will and will not be successful in an automated workforce, namely preexisting wealth and relevant skills. Primarily, success in an economy of increasing automation and artificial intelligence will be highly dependent on socioeconomic status and technological proficiency. Those who are already in the upper class of society have the resources to remain there. Likewise, those with the skills to facilitate the efficiency of technology, rather than the large majority who simply hinder it are inherently predisposed to fair better in a developing technological economy. Specifically, when we consider that “machines will replace some labourers and augment the value of others” along with the idea that“not only must a person be bright, he must be bright in certain approved, useful directions”, it is apparent that a small minority of workers will be more greatly liable to thrive in this new workplace environment (Cowen 127 and 137). By incorporating both of these factors, we can see that a person’s current standing of wealth or poverty will likely affect the extent to which proficiency in technology will influence their economic class. That is to say, the pre-existing wealth gap will tend to favour wealthy people who can adapt to a technology-driven market the most strongly, and will cause the most distress to those in lower classes who will not be augmented by technology. If we consider the already existing division of classes, we can see that the economies of many nations are already predisposed to the further divisions. The wealth gap that already exists will serve to facilitate an increase in the division of classes, brought on by automation and artificial intelligence. Rather than the complete development of a new economic and social order, it appears that technology will act towards an exacerbation of the existing situation. Wealth polarization is an existing problem that will only be intensified by the further changes that will be brought on by automation. Overall, from current trends in manufacturing and the factors that will affect employability and success in a more technology driven world, we can see that automation and artificial intelligence are poised to exacerbate the state of inequality that already exists on a national level. As technology becomes more advanced, it will begin to displace an increasingly large amount of employs, while in turn benefiting a very small subset of the current workforce. The unequal benefits provided by technology could very likely lead to extensive polarization of wealth and an extreme division between newly defined socioeconomic classes. Unfortunately, these new classes are likely to be disposed towards the already existing situation of inequality. A similar trend can be seen when we examine the effects of automation and artificial intelligence on a global scale.

II) Global Level Much like the widening inequality that is likely to occur between classes on a national scale, automation and artificial intelligence are also capable of having an impact on the inequality between nations. When we consider automation and artificial intelligence in a global context, the predominant problem that arises is a widening disparity between developed and developing countries. Due to differences in the distribution of employment between wealthy and poor countries, developing economies rely more heavily on manual labour oriented jobs. This in turn makes them more vulnerable to the advancements brought on by automation and artificial intelligence. Developing countries have distributions of employment that are vastly different from those of developed countries. The inherent lack of infrastructure in poor countries means that they rely on manual labour to accomplish many tasks that are delegated to machines or machine-aided workers in more wealthy countries. The consequence of this is that the introduction of automation and artificial intelligence to developing countries will have a much greater effect than within developed nations. Agriculture is one economic sector in which this is particularly evident. It has been automated in most developed countries, to the point where agriculture made up only 1.7% of the United States’ employment in 2002 (Banister 25). In the same year, agriculture made up 48.2% of the employment in China (Banister 25). Essentially, in developing countries “…the service sector is comparatively underdeveloped and agriculture continues to employ more workers than services” (Banister 25). This means that, while automation will definitely affect inequality in developed nations, in developing nations a larger percentage of people are at risk of being replaced by automation and artificial intelligence in the near future. The inequality of wealth that currently exists between developed and developing economies plays a large role in making poor countries more susceptible to the possible detrimental consequences of increased automation and artificial intelligence.

Further exacerbating this problem is the fact that the majority of meaningful development occurring in poor countries is in economic sectors that are easily replaced by automation and artificial intelligence. Despite a downturn in manufacturing employment from 1995- 2000, which corresponds with trends in countries all around the world, manufacturing employment in China has actually been on the rise since 2001 (Banister 26). While it goes completely against the employment trends in developed countries, which have experienced a marked decrease in manufacturing employment, this pattern is not unusual among developing countries (Banister 26). As poor countries begin to develop, they go through their own equivalent of an industrial revolution (Banister 27). This is an important step to becoming more developed, but also correlates with a large increase in manufacturing jobs. As a consequence, the growth that is purported to be shrinking the gap between wealthy and poor nations may actually lead to a greater disadvantage for developing countries. Increased use of technology could completely eliminate the majority of economic progress that has been made in developing countries. The root problem of increased automation and use of artificial intelligence on a global scale is that developing countries, which are already at an economic disadvantage, are currently positioned to suffer greater employment loss than developed countries. The major concern on a global level is that an increase of technology will not affect all countries equally. Specifically, developing countries will suffer an inordinate loss of employment and source of income when compared developed countries. This is an especially difficult problem because the very factors that designate a country as “developing”, inherently lead it to being more vulnerable. In turn, developing countries will become further distanced from the economic success of developed countries. Technology will once again act as a divisive factor in terms of separating the wealthy form the poor.

III) Overview The increasing role of automation and artificial intelligence poses a large problem on a national and global level. In both cases technology facilitates an economic divide between groups by way of advantaging or disadvantaging groups to various degrees. This is an issue because the damage that technology may cause will not be equally distributed. In fact, in most cases it is those who are already disadvantaged that stand to lose the most as a result of a technology driven economy. In order to prevent these potential futures from becoming a reality, we should take steps to alter the current situation.

What are some Solutions?

Automation and artificial intelligence are emerging technologies, which means that the problems associated with them are yet to develop fully. As a result of this, there have not been solutions proposed for the specific problems likely to be caused by automation and artificial intelligence. At the current time, the most feasible course of action is to address the situations that may eventually play a role in worsening the problems linked to increasing usage of technology. Primarily, this includes combating inequality on a national and global level, as well as developing technology in a responsible way. Reducing inequality will be an important step towards mitigating the problems associated with automation and artificial intelligence. However, this is more easily said than accomplished. As we have seen, inequality is a deeply ingrained problem with a number of contributing factors. Reducing inequality will require a long and sustained effort from governments on multiple levels. Specifically, Cummins and Ortiz propose that in high-income economies it will be important to introduce, “public policies that focus on generating employment and household income, ensuring access to land and assets, as well as infrastructure and services”(36). The idea behind this is to improve equality by “expanding domestic markets as a strategy to raise demand and promote economic growth” (Cummins and Ortiz 36). Similarly, they suggest that developing countries should focus on “policies that support employment and invest in education, water supply, sanitation, food security, and nutrition”, as a means of strengthening and equalizing their economy (Cummins and Ortiz 37). By following these proposed courses of action we may be able to reduce inequality and limit the issues that will arise from increased automation and use of artificial intelligence.

In addition to this, if we are mindful of the way in which we develop automation and technology, we may be able to prevent the dramatic levels of inequality that would otherwise result from technological development. Rather than “the advances of genius machines com[ing] in an uneven and staggered fashion,” we could limit the extent to which we allow technology to progress (Cowen 119). In this situation, by controlling the technology that is available for public use, governments or outside regulatory bodies would ensure that technology only progressed in a way that would minimize the potential for inequality. By this method, we could effectively prevent the greatest problems linked to automation and artificial intelligence while maintaining a level of technological development. By addressing the situations that will likely be intensified by automation and artificial intelligence, we may mitigate the impact that developing technology will have in the future. In both of these strategies the goal is to solve the problems caused by automation and artificial intelligence before they can become fully formed. If we don’t take action soon, technology may progress to the point were a solution seems impossible. It is important that we act as soon as possible to ensure the stability of equality on a national and global level.

Conclusion

In conclusion, we have seen that automation and artificial intelligence may increase the level of inequality present on both a national and global level. We have also gained an understanding of the problems that lie at the root of this situation. Namely, the uneven development of technology and the existing wealth gap between socioeconomic classes and countries seem to be the main causes of increased inequality. Both of these factors generate situations in which automation and artificial intelligence are more likely to advantage a small group, while disadvantaging a large reminder of the population. It has been suggested that the most effective way to prevent these effects is to mitigate these causative situations before they can begin to play a significant role when related to automation and artificial intelligence. As these technologies progress it is important that we have an understanding of the gives us the opportunity to prevent a catastrophic loss of employment, and a subsequent reshaping of national and global economics. Rather than being paralyzed by the apparent complexity of the problems facing us, it is vital that we take small steps towards preventing dramatically increased inequality. Automation and artificial intelligence are powerful tools, but we need to use them carefully.

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