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Negative Faces of Artificial Intelligence in the Conditions of the Knowledge-Based Economy

Author

Listed:
  • Artur Kwasek
  • Maria Kocot
  • Izabela Gontarek
  • Igor Protasowicki
  • Bartosz Blaszczak

Abstract

Purpose: The purpose of this article is to identify and analyze concerns related to the use of artificial intelligence (AI) in the Knowledge Economy (KBE) environment. The study focuses on understanding which aspects of AI technology are the most feared in society and how these concerns interact. Design/Methodology/Approach: The survey was conducted in January 2024 on a sample of 956 students from three universities in Poland. A survey method was used in which respondents rated their concerns about various negative aspects related to AI on a five-point Likert scale. The data were statistically analyzed to determine the level of concern in each category and the correlations between them. Findings: The results of the survey show that respondents' greatest concerns are about AI taking control of ICT systems and the potential impact of AI on mass unemployment and social inequality. A significant number of respondents also expressed concerns about the takeover of humanity and the destruction of humanity by advanced AI systems. Correlational analysis revealed that these concerns are strongly linked, suggesting that risk perceptions in different areas influence each other. Practical Implications: Understanding AI concerns in the context of GOW is essential to developing risk management strategies and creating regulations that ensure the safe and ethical implementation of AI. The results of the research can help policymakers identify key areas for intervention and take action to increase public awareness of the potential risks of AI. Originality/Value: The article makes a unique contribution to the literature by focusing on the negative aspects of AI in the context of the Knowledge Economy and analyzing the perception of fears among students who constitute the future management and decision-making staff. The study provides new insights into the interconnectedness of AI concerns, which could provide a basis for further research and discussion on the ethical and social implications of AI deployment.

Suggested Citation

  • Artur Kwasek & Maria Kocot & Izabela Gontarek & Igor Protasowicki & Bartosz Blaszczak, 2024. "Negative Faces of Artificial Intelligence in the Conditions of the Knowledge-Based Economy," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 465-477.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:2:p:465-477
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    References listed on IDEAS

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    1. Leonel Prieto & Md Farid Talukder, 2023. "Resilient Agility: A Necessary Condition for Employee and Organizational Sustainability," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    2. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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    More about this item

    Keywords

    Artificial intelligence; Knowledge Economy; ICT technology.;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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