Analysis of Artificial Intelligence and Cognitive Automation on Employment of High Skilled Professions

Author:
Yang, David, School of Engineering and Applied Science, University of Virginia
Advisors:
Fitzgerald, Gerard, EN-Engineering and Society, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Abstract:

This paper examines the relationship between Artificial Intelligence and unemployment, focusing on automation of cognitive tasks such as high skilled professions. The fast-paced integration of Artificial Intelligence in industries is expected to have a perpetual effect on societal processes. While it brings opportunities for innovation, efficiency, and economic growth, it also creates challenges related to job automation and ethical dilemmas. Balancing the benefits of AI with responsible deployment and addressing its potential impacts firsthand will be important for achieving the full potential of this transformative technology.
To analyze this issue, this paper illustrates the expert viewpoints and predictions of artificial intelligence from multiple perspectives and analyzes the implications for our society with respect to AI and employment using Actor Network Theory. Actor-Network Theory (ANT) is a sociological and philosophical framework that challenges traditional perspectives on social theory by acknowledging that both living and non-living entities contribute to the structure of society. By employing the Actor-Network Theory (ANT), we can use past instances of such technologies that brought about change in our society and analyze the complex network of processes between actors, both human and non-human, and gain insights into how AI will influence and restructure our society.
This paper will first delve into how the society of the previous generation has fared with the emergence of a new innovative technology. The surge of digital technologies have transformed industries and reshaped the labor market with mass automation in the 20th century, particularly affecting low-skilled workers. However, As automation targets routine-based work, the 4th industrial revolution diverges from its predecessors, impacting high-skilled occupations. The mounting fear among these professionals underscores the unique challenges by the 4th industrial revolution, hinting at extreme shifts in socioeconomic stability and wealth distribution. This paper explores the differences between the human and non-human actors involved in these two distinct emerging technologies and puts them in the perspective of ANT to analyze what it could imply for future workers and the labor market.
After the analysis, this paper will examine computerization of various complex cognitive tasks with the utilization of big data and analyze the range of impact in the industry. This includes an examination into the development of specialized algorithms for big data analysis, emphasizing the scalability advantage of computers. This paper also explores multiple literature of case studies on current cognitive automation in certain job sectors. By examining current integrations of AI in our societal processes and assessing its effects, it will be possible to observe the potential impact of cognitive automation on various technological fields.
The paper will highlight the impact of offloading tasks to cognitive automation, emphasizing the benefits of freeing human cognitive capacity while cautioning against potential drawbacks such as skill erosion. It underscores the specific risks in fields requiring expertise and the broader consequences for organizations, including the potential for negative outcomes and loss of competitiveness when knowledge capital erodes. The analysis will involve a case study on an organizational failure due to skill erosion.

Degree:
BS (Bachelor of Science)
Keywords:
Artificial Intellignece, Actor Network Theory
Notes:

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Briana Morrison

STS Advisor: Gerard Fitzgerald

Language:
English
Rights:
All rights reserved (no additional license for public reuse)
Issued Date:
2024/05/07