Fear of the Future in the Context of the Emergence of Generative Artificial Intelligence Among Young Adults: The Case of Czechia
Abstract This article delves into the fears and perceptions of young adults in Czechia regarding the uncertain future shaped by the rapid emergence of Generative Artificial Intelligence (GenAI). The analysis is based on a structured digital survey of 500 respondents within the 15-24 age group, predominantly university students. The survey used a dual-scale question design combining five-point Likert scales to measure levels of agreement and parallel importance ratings for each statement. Participants shared their views on GenAI’s potential effects on critical societal dimensions such as national sovereignty, democratic processes, individual privacy, and the labor market. Statistical analyses, including t-tests and correlation analyses, were conducted to identify subgroup differences and associations with self-reported digital competence. The findings reveal gender-based disparities, with female respondents consistently reporting higher levels of concern across the examined areas. These results suggest that gender plays a significant role in shaping perceptions of technological risks. Despite minor correlations suggesting that higher digital competence slightly reduces GenAI-related fears, the overall perception of risk re-mains high, particularly among females. Such a perspective aligns with the prevailing biases and adversarial use of technology that shape the perception of a GenAI-driven future. These results emphasize the need for policymakers to adopt gender-sensitive strategies when crafting regulations and frameworks for the responsible development and deployment of GenAI technologies. By addressing the diverse concerns of various demographic groups, society can better navigate the challenges posed by GenAI and foster a more inclusive and equitable technological future.
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Auer, R., Köpfer, D., & Švéda, J. (2024). The rise of generative AI: Modelling exposure, substitution, and inequality effects on the US labour market.
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-022-05200-2
Bessen, J. E. (2019). AI and jobs: The role of demand. National Bureau of Economic Research. https://www.nber.org/papers/w24235
Blau, F. D., & Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature, 55(3), 789–865. https://doi.org/10.1257/jel.20160995
Brauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2024). AI perceptions across cultures: Similarities and differences in expectations, risks, benefits, tradeoffs, and value in Germany and China. arXiv preprint arXiv:2412.13841.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., ... & Tavares, M. M. M. (2024). Gen-AI: Artificial intelligence and the future of work. International Monetary Fund. https://doi.org/10.5089/9798400262548.006
Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly.
Cutter, S. L., Tiefenbacher, J., & Solecki, W. D. (1992). Engendered fears: Femininity and technological risk perception. Industrial Crisis Quarterly, 6(1), 5–22.
De Welde, K., & Laursen, S. (2011). The glass obstacle course: Informal and formal barriers for women Ph.D. students in STEM fields. International Journal of Gender, Science and Technology, 3(3), 571–595.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, e101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1). https://doi.org/10.1111/j.1083-6101.2000.tb00110.x
Ellingrud, K., Sanghvi, S., Madgavkar, A., Dandona, G. S., Chui, M., White, O., & Hasebe, P. (2023). Generative AI and the future of work in America.
Fast, E., & Horvitz, E. (2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://ojs.aaai.org/index.php/AAAI/article/view/10824
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Frimpong, V. (2024). Cultural and regional influences on global AI apprehension. Qeios, 6(11). https://doi.org/10.32388/YRDGEX.3
Gustafson, P. E. (1998). Gender differences in risk perception: Theoretical and methodological perspectives. Risk Analysis, 18(6), 805–811.
Hajdu, D., Klingová, K., Karaz, J., Musilová, V., & Szicherle, P. (2024). GLOBSEC Trends 2024: CEE – A Brave New Region? GLOBSEC.
Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., ... & Zwitter, A. (2019). Will democracy survive big data and artificial intelligence? In Towards Digital Enlightenment (pp. 73–98). Springer, Cham.
Hill, C., Corbett, C., & St. Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women.
Howley III, R. J. (2019). The effects of artificial intelligence on the youth. Utica College.
Ji, Y., Chen, L., Wang, L., Hou, J., Chen, X., & Zhu, H. (2023). Generative AI’s labor-replacing impacts on occupations also foster short-run job opportunities for early adopters. SSRN. https://ssrn.com/abstract=4862800
Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., ... & Hill, C. (2021). Impact of big data and artificial intelligence on industry: Developing a workforce roadmap for a data driven economy. Global Journal of Flexible Systems Management, 22(3), 197–217.
Karnouskos, S. (2020). Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3), 138–147.
Leaper, C., & Starr, C. R. (2019). Helping and hindering undergraduate women’s STEM motivation: Experiences with STEM encouragement, STEM-related gender bias, and sexual harassment. Psychology of Women Quarterly, 43(2), 165–183.
Madgavkar, A., Manyika, J., Krishnan, M., Ellingrud, K., & Yee, L. (2019). The future of women at work.
McKinsey Global Institute (2017). A future that works: Automation, employment, and productivity. https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works
Monge-Rodríguez, F. S., Jiang, H., Zhang, L., Alvarado-Yepez, A., Cardona-Rivero, A., Huaman-Chulluncuy, E., & Torres-Mejía, A. (2021). Psychological factors affecting risk perception of COVID-19: Evidence from Peru and China. International Journal of Environmental Research and Public Health, 18(12), 6513. https://doi.org/10.3390/ijerph18126513
Moy, W. R., & Gradon, K. T. (2023). Artificial intelligence in hybrid and information warfare: A double-edged sword. In Artificial Intelligence and International Conflict in Cyberspace (pp. 47–74). Routledge. https://doi.org/10.4324/9781003284093-4
Nader, K., Toprac, P., Scott, S., & Baker, S. (2024). Public understanding of artificial intelligence through entertainment media. AI & Society, 39(2), 713–726. https://doi.org/10.1007/s00146-022-01427-w
Nadim, M., & Fladmoe, A. (2021). Silencing women? Gender and online harassment. Social Science Computer Review, 39(2), 245–258.
Nguyen, D., & Hekman, E. (2024). The news framing of artificial intelligence: A critical exploration of how media discourses make sense of automation. AI & Society, 39(2), 437–451. https://doi.org/10.1007/s00146-022-01511-1
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
Santoni de Sio, F., & Mecacci, G. (2021). Four responsibility gaps with artificial intelligence: Why they matter and how to address them. Philosophy & Technology, 34(4), 1057–1084.
Savadori, L., & Lauriola, M. (2021). Risk perception and protective behaviors during the rise of the COVID-19 outbreak in Italy. Frontiers in Psychology, 11, e577331. https://doi.org/10.3389/fpsyg.2020.577331
Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Tifferet, S. (2019). Gender differences in privacy tendencies on social network sites: A meta-analysis. Computers in Human Behavior, 93, 1–12.
Vitezić, V., & Perić, M. (2024). The role of digital skills in the acceptance of artificial intelligence. Journal of Business & Industrial Marketing. https://doi.org/10.1108/JBIM-04-2023-0210
West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI. AI Now Institute.
Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. https://www.fhi.ox.ac.uk/wp-content/uploads/Artificial-Intelligence-American-Attitudes-and-Trends.pdf
Hruška, A., & Roubík, H. (2025). Fear of the Future in the Context of the Emergence of Generative Artificial Intelligence Among Young Adults: The Case of Czechia. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e32644. https://doi.org/10.14201/adcaij.32644
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