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Agrawal, A, Gans, J. S. & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labour market impact of automating prediction. The Journal of Economic Perspectives, 33(2), 31-50. Web.
The article in question considers the impact the spread of artificial intelligence technology may have on the labor market. The authors define their goal as to establish and define the way artificial intelligence influences the job market and ratio of work tasks distribution. In addition, they are determined to predict the further course of events concerning the jobs that will be affected the most. The authors state that though machines can substitute workers, they lack one significant characteristic every human being possesses. It is called cognition or, in other words, the ability to make decisions. Decision-making goes hand-in-hand with prediction, which artificial intelligence is already capable of. Throughout the whole article, the authors assess the influence of artificial intelligence on both these aspects. The authors explicitly state that the artificial intelligence technology may substitute people who work on prediction tasks such as weather forecasts or human resources area since the process of documents-related work can be automized.
Throughout the whole article, the authors see their task as to highlight the necessity of thinking regarding prediction and decision-related tasks where prediction has no value without decision. That is why it is necessary to assess if the process related to a particular work activity involves both prediction and decision. The authors stress that if people who perform prediction-related tasks are substituted by artificial intelligence, the decision-related sphere will be affected as well. The influence may be positive or negative since the changes may cause either the work tasks downstream or upstream. The authors repeatedly address the ambiguity of the issue throughout the article because artificial intelligence technology is constantly developing, so it is difficult to assess its abilities adequately.
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