Artificial intelligence is evident in the online world of today, by invisibly sitting behind online commercial websites and research engines, it is already a part of our daily lives. It offers possibilities to enable a more effective governance and commercial enterprise, but instead, the application of artificial intelligence results in significant questions about the government, ethical code and accountability. We conceive that economic analysis, past events and logic, propelled by artificial intelligence as well as robotics, all point to a conclusion that the future technological advancement, will not cause work exhaustion and above average unemployment levels. Nevertheless, it could increase rate of growth of labor productivity, making the society prosperous, and increasing incomes per person, for all the Americans. Intrinsically, policymakers should take initiatives to support AI by utilizing it more widely within government functions instead of giving in to the growing techno-scare over AI or taking initiatives to slow down the advancement of AI.
Lastly, while the future innovation wave won’t lead to joblessness, it will most probably raise the labor market, causing the state governments in the United States to equip employees with the essential equipment, moral support and skills they require to navigate in a labor market which is disorderly. This research will discuss on the following;
Impact of AI on the overall employment and salaries.
Impact of AI on a particular group of workers.
Impact of AI on the labor market structure.
Impact of AI on the overall employment and salaries
Researchers propose that, in a new field of research, their work offers initial findings, whereby extra empirical and theoretic work is required (Korinek " Stiglitz 2017). Beyond any former replacement impacts that may cause displacement of workers, the articles reexamined, are nonetheless functional to demonstrate the technical aspects by which Artificial Intelligence is anticipated to impact on occupations. These researchers come to the conclusion that, in the short-run, workers might be worse off due to the domination of the replacement impact (Korinek " Stiglitz 2017). Nevertheless, the productivity impact is perpetually strong enough that it can begin the replacement effect in the long-run. Linked to the pre-automation circumstance, is the salary increase. The portion of income field of economics acquired by workers in relation to owners may decrease due to an increasing ratio of a machine-controlled economic activity, that is, brought about by an increase in capital and less human labor (Bresnahan et al 2002). According to the analysis given, AI’s short-term impact, relies on the correspondence between productivity impact, innovating new jobs as well as the replacement effect (Bresnahan et al 2002). Perhaps unreasonably, more turbulent automation might have a more benignant impact on laborer’s, rather than technology which is only replaces workers, but non-effective in raising productivity considerably. This discussion makes comparisons of a former situation with no automation and a new setting where there is introduction of greater automation.
According to Korinek " Stiglitz (2017), during the time of transition, unemployment might increase even though the counteracting impacts seen above may set off displacement. New tasks may need different skills as compared to tasks that are changed due to automation. According to Dwyer (2013) the Great Depression in the United States is due to unemployment partially related to the issues brought about by the transition to manufacturing from agriculture. Korinek " Stiglitz (2017), propose that government intercession enabled the accompanying structural change.
Impact of AI on a particular group of workers
Bessen (2018), alleges that, whatever happens to workers in particular industries, is determined by how consumers respond to productivity impacts. Bessen’s theory suggests that faster acceptance of mechanization does not inevitably lead to the loss of jobs in the automating industry. Most probably, not only does the results depend on the capacity and speed of acceptance of technology, but also on the user’s reaction, although it is important to acknowledge that in a universe which is globalized, options of users in a nation may have less impact over patterns of global demand. In this section, the discourse regards workers as one similar group. In contrast, historical proof indicates that technology impacts different people, differently (Bessen 2018).
Inequality within the economy could increase if technology specifically interchanges main tasks done by low educated workers, either during the short or long run, despite describing of the above offsetting mechanisms. There are two types of workers identified by Acemoglu " Restrepo (2011), they include, ‘high-skilled’ and ‘low-skilled’ workers, and field economics theory of automation which might replace workers in jobs done by either of the groups. During the short run, technology replacing one workers group for e.g. the highly skilled results to a decrease in income of workers displaced. The income of former workers group for e.g. the lowly skilled, could also decrease, since the workers who have been displaced compete for tasks with the group which has not been displaced (Bessen 2018). In the long run, there is an increase in the average salaries as compared to the non-automation baseline. The productivity impacts lead to displacement, due to consistency with the theory focusing on one group of workers. Despite this, the displaced group experience a decrease in income proportional to the non-displaced group, and possibly even in perfect conditions. Acemoglu " Auto (2011) make an assumption that productivity impacts all groups equally, but disregarding this assumption, productivity improvement reactions could increase demand disproportionately, for the industry products that hires a specific high ratio of lowly skilled workers.
Impact of AI on the labor market structure
This section discusses about ‘job polarization’. According to Dwyer (2013), ‘Job polarization’ refers to the increasing number of high education and low education tasks along the decrease in the number of ‘middle education tasks. Simultaneously, there is an increasing inequality in incomes, particularly, the increased distance between workers who have a high income and the other workers.
A task-based theory of the economy has demonstrated the mechanism through which technology contributes to polarization. This theory is coherent with proof on the description of polarization, whereby high-education and low-education tasks benefit over the average-education tasks, and incomes of people who are highly educated develop proportionally to other laborers. The increasing function of technology in the process of production helps in explaining job polarization, this is according to the evidence generated by Empirical work. Dwyer (2013) found proof of polarization of jobs in the United Kingdom between 1975 and 1999 as a result, consequences such as, increasing high-education tasks has been witnessed across industries and also decline in the average-education jobs, because of decline in the industry for e.g. operation of machines becoming less significant in the manufacturing sector as well as change in employment towards services and further from manufacturing.
Low-education jobs have increased, as a result of rising both within and between industries (Dauth et al 2017). It has also been discovered that workers who are highly educated, have increasingly clumped together across the United States and other progressed economic regions due to job polarization, (Dwyer 2013). There is an increased gap between the highly educated workers and the others not only in income but also in localization. In future, the growth of jobs associated with AI might also be agglomerated in different regions compared to the ones impacted by the decrease in any tasks connected to AI. As a result, this could lead to issues, specifically if given proof that workers who are low-educated will less probably relocate because of possible job opportunities as compared to high-educated workers (Dwyer 2013).
Conclusion
In conclusion, proof from various disciplines indicates that AI’s impact will most probably be determined not only through technological advancement, but also through societal, religious and economic elements. Organization of jobs can be altered significantly across borderlines even if a similar technology is applied in production process. There is proof that work has been affected on its purpose of liberalization of trade due to digital technology and automation. This proof demonstrates that the utilization of digital automation in work is associated with the increase in job polarization between works mainly done by workers who are low-educated and tasks performed by workers who are high-educated. There has not been a fall in employment, but instead, there have been changes from industrial activities to the provision of services, and also losses in income for workers who are lowly educated and displaced across geographical areas. Studies propose that AI could impact a minority of existent tasks, and that work done by workers who are low-educated are likely to be impacted compared to work done by workers who are high-educated. Economic modelling demonstrates that possible loss of jobs during short term will be remunerated by offsetting techniques through which production increase leading to a higher labor demand. As a result, this may cause a substantial increase in cases of inequality, specifically if employers control market power.
References
Acemoglu, D. and Autor, D., 2011. Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4, pp. 1043-1171). Elsevier.
Bessen, J.E., 2018. Automation and jobs: When technology boosts employment.
Bresnahan, T.F., Brynjolfsson, E. and Hitt, L.M., 2002. Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. The Quarterly Journal of Economics, 117(1), pp.339-376.
Dauth, W., Findeisen, S., Südekum, J. and Woessner, N., 2017. German robots-the impact of industrial robots on workers.
Dwyer, R.E., 2013. The care economy? Gender, economic restructuring, and job polarization in the US labor market. American Sociological Review, 78(3), pp.390-416.
Korinek, A. and Stiglitz, J.E., 2017. Artificial intelligence and its implications for income distribution and unemployment (No. w24174). National Bureau of Economic Research.
