Potential Solutions for Job Displacement by Artificial Intelligence
Main Article Content
Abstract
The emergence and rapid adoption of artificial intelligence (AI) across industries has raised concerns about workplace displacement and the future role of humans in the workforce. To address these concerns, the efficacy of potential solutions for job displacement caused by AI were examined, focusing on government intervention, workforce adaptation, and public resistance to AI. A narrative review of literature showed that governments could address job displacement caused by AI by implementing an AI taxation policy, by developing social safety nets for displaced workers, and by updating education systems. The literature also showed that workforce members could also be an important part of the solution by purposefully adapting to AI through lifelong learning, by developing a growth mindset, and by becoming more familiar with AI. Finally, public resistance to AI was found to be a valuable strategy for monitoring and responding to unethical companies, making them more accountable when using AI, and for strengthening policies to protect employees. Based on this evidence, it is clear that government agencies should design and implement policies to prevent workforce displacement caused by AI implementation. Such policies should pressure companies to develop fair and sustainable AI practices and provide opportunities for employee retraining.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสาร ถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรง ซึ่งกองบรรณาธิการวารสารไม่จำเป็นต้องเห็นด้วย หรือร่วมรับผิดชอบใด ๆ
References
Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488-1542. https://doi.org/10.1257/aer.20160696
Adhikari, P., Hamal, P., & Jnr, F. B. (2024). Impact and regulations of AI on labor markets and employment in USA. International Journal of Science and Research Archive, 13(1), 470-476. https://doi.org/10.30574/ijsra.2024.13.1.1670
Ampofo, J. W., Emery, C. V., & Ofori, I. N. (2023). Assessing the level of understanding (knowledge) and awareness of diagnostic imaging students in Ghana on artificial intelligence and its applications in medical imaging. Radiology Research and Practice, 2023(1), 4704342. https://doi.org/10.1155/2023/4704342
Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30. https://doi.org/10.1257/jep.29.3.3
Banerjee, S., Alsop, P., Jones, L., & Cardinal, R. N. (2022). Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies. Patterns, 3(6), 100506. https://doi.org/10.1016/j.patter.2022.100506
Bănescu, C.-E., Titan, E., & Manea, D. I. (2023). Continuous learning: The solution to stay on digital labour market. Management and Economics Review, 8(2), 237-245. https://doi.org/10.24818/mer/2023.06-08
Barrera, R. (2009). Crisis! jobless and small business: Danger and hope. Proceedings of the 53rd Annual Meeting of the ISSS (pp. 1-12). https://journals.isss.org/index.php/proceedings53rd/index
Bessen, J. (2018). AI and jobs: The role of demand (NBER Working Paper No. 24235). National Bureau of Economic Research. https://doi.org/10.3386/w24235
Böhmer, N., & Schinnenburg, H. (2023). Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations: The International Journal, 45(5), 1057-1082. https://doi.org/10.1108/er-04-2022-0202
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Capinding, A. T., & Dumayas, F. T. (2024). Transformative pedagogy in the digital age: Unraveling the impact of artificial intelligence on higher education students. Problems of Education in the 21st Century, 82(5), 630-657. https://doi.org/10.33225/pec/24.82.630
De Cremer, D. (2022). With AI entering organizations, responsible leadership may slip! AI and Ethics, 2(1), 49-51. https://doi.org/10.1007/S43681-021-00094-9
Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A view from two eras. Perspectives on Psychological science, 14(3), 481-496. https://doi.org/10.1177/1745691618804166
Ekuma, K. (2023). Rethinking Upskilling and Reskilling in the Age of AI and Automation: A fsQCA Approach. Preprints. https://doi.org/10.20944/preprints202309.0055.v1
Faishal, M., Mathew, S., Neikha, K., Pusa, K., & Zhimomi, T. (2023). The future of work: AI, automation, and the changing dynamics of developed economies. World Journal of Advanced Research and Reviews, 18(3), 620-629. https://doi.org/10.30574/wjarr.2023.18.3.1086
Farrow, E. (2021). Mindset matters: how mindset affects the ability of staff to anticipate and adapt to Artificial Intelligence (AI) future scenarios in organisational settings. AI & Society, 36(3), 895-909. https://doi.org/10.1007/s00146-020-01101-z
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020). Public perception of artificial intelligence in medical care: content analysis of social media. Journal of Medical Internet Research, 22(7), e16649. https://doi.org/10.2196/16649
Gonçalves, B. (2023). The Turing test is a thought experiment. Minds and Machines, 33(1), 1-31. https://doi.org/10.1007/s11023-022-09616-8
Höyng, M., & Lau, A. (2023). Being ready for digital transformation: How to enhance employees’ intentional digital readiness. Computers in Human Behavior Reports, 11, 100314. https://doi.org/10.1016/j.jsis.2024.101885
Ibegbulam, C., Olowonubi, J., Fatounde, S., & Oyegunwa, O. (2023). Artificial intelligence in the era of 4IR: drivers, challenges and opportunities. Engineering Science & Technology Journal, 4(6), 473-488. https://doi.org/10.51594/estj.v4i6.668
Jaiswal, A., Arun, C. J., & Varma, A. (2023). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. In A. Malik & P. Budhwar (Eds.), Artificial intelligence and international HRM: Challenges, opportunities and a research agenda (pp. 114–143). Routledge. https://doi.org/10.4324/9781003377085-5
James, D., Sadik, S., & Brown, P. (2023). Rethinking Lifelong Learning in the “Fourth Industrial Revolution”. In K. Evans, W. O. Lee, J. Markowitsch, & M. Zukas (Eds.), Third international handbook of lifelong learning (pp. 1091–1110). Springer International Publishing. https://doi.org/10.1007/978-3-031-19592-1_49
Kim, W., Choi, Y., Lee, T.-H., Jang, S.-Y., Han, K.-T., & Park, E.-C. (2018). Job displacement and social safety net on depressive symptoms in individuals aged 45 years or above: Findings from the Korean longitudinal study of aging. Ageing & Society, 38(6), 1199-1222. https://doi.org/10.1017/s0144686x16001471
Kopka, A., & Grashof, N. (2022). Artificial intelligence: Catalyst or barrier on the path to sustainability? Technological Forecasting and Social Change, 175, 121318. https://doi.org/10.1016/j.techfore.2021.121318
Kovacev, R. J. (2020). A taxing dilemma: robot taxes and the challenges of effective taxation of AI, automation and robotics in the fourth industrial revolution. The Ohio State Technology Law Journal, 16(2), 182-218.
Krskova, H., & Breyer, Y. A. (2023). The influence of growth mindset, discipline, flow and creativity on innovation: Introducing the MDFC model of innovation. Heliyon, 9(3). e13884 https://doi.org/10.1016/j.heliyon.2023.e13884
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
Liu, J. (2024). The impact of the development of artificial intelligence on unemployment rates. Advances in Economics, Management and Political Sciences, 121(1), 154-163. https://doi.org/10.54254/2754-1169/121/20242410
Marku, M. (2024). Navigating the future of work: human capital in the age of industry 4.0. Interdisciplinary Journal of Research and Development, 11(1), 190-195. https://doi.org/10.56345/ijrdv11n127
McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
Mishra, D. (2024). Strategic communication for AI-driven sustainability initiatives: Bridging technology, policy, and public engagement in achieving SDGs. In B. Riswandi, B. Singh, C. Kaunert, & K. Vig (Eds.), AI applications for clean energy and sustainability (pp. 187–211). IGI Global. https://doi.org/10.4018/979-8-3693-6567-0.ch010
O'neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Pedota, M., Grilli, L., & Piscitello, L. (2023). Technology adoption and upskilling in the wake of Industry 4.0. Technological Forecasting and Social Change, 187, 122085. https://doi.org/10.1016/j.techfore.2022.122085
Peoples, L. F. (2025). Artificial intelligence and legal analysis: Implications for legal education and the profession. Law Library Journal, 117(1), 52-85.
Rikala, P., Braun, G., Järvinen, M., Stahre, J., & Hämäläinen, R. (2024). Understanding and measuring skill gaps in Industry 4.0—A review. Technological Forecasting and Social Change, 201, 123206. https://doi.org/10.1016/j.techfore.2024.123206
Robles, P., & Mallinson, D. J. (2025). Artificial intelligence technology, public trust, and effective governance. Review of policy research, 42(1), 11-28. https://doi.org/10.1111/ropr.12555
Sahoh, B., & Choksuriwong, A. (2023). The role of explainable Artificial Intelligence in high-stakes decision-making systems: A systematic review. Journal of Ambient Intelligence and Humanized Computing, 14(6), 7827-7843. https://doi.org/10.1007/s12652-023-04594-w
Saidakhror, G. (2024). The impact of artificial intelligence on higher education and the economics of information technology. International Journal of Law and Policy, 2(3), 1-6.
Shrivastava, A., Pandey, A., Singh, N., Srivastava, S., Srivastava, M., & Srivastava, A. (2024). Artificial intelligence (AI): Evolution, methodologies, and applications. International Journal for Research in Applied Science and Engineering Technology, 12(4), 5501-5505.
Siregar, R. A. (2023). A legal perspective on the transformation of health services with artificial intelligence. Soepra Jurnal Hukum Kesehatan, 9(2), 306-314. https://doi.org/10.24167/sjhk.v9i2.11270
Srivastava, J., Dixit, A., & Narayan, J. (2023). Artificial intelligence and the legal profession. In International Conference on Green Energy, Computing and Intelligent Technology (pp. 366–371). Institution of Engineering and Technology.
Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., & Jiya, T. (2022). Organisational responses to the ethical issues of artificial intelligence. AI & Society, 37(1), 23-37. https://doi.org/10.1007/s00146-021-01148-6
Susskind, R., & Susskind, D. (2022). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Teuscher, C. (2012). Foreword: Special issue on Alan Turing. Evolutionary Intelligence, 5(1), 1-2. https://doi.org/10.1007/S12065-011-0063-2
Tiwari, R. (2023). The impact of AI and machine learning on job displacement and employment opportunities. International Journal of Engineering Technologies and Management Research, 7(1), 1-9. https://doi.org/10.55041/ijsrem17506
Wallén, J. (2008). The history of the industrial robot. Linköping University Electronic Press.
Wang, K.-H., & Lu, W.-C. (2025). AI-induced job impact: Complementary or substitution? Empirical insights and sustainable technology considerations. Sustainable Technology and Entrepreneurship, 4(1), 100085. https://doi.org/10.1016/j.stae.2024.100085
Yigitcanlar, T., Degirmenci, K., & Inkinen, T. (2024). Drivers behind the public perception of artificial intelligence: insights from major Australian cities. AI & Society, 39(3), 833-853. https://doi.org/10.1007/s00146-022-01566-0
Zhao, T. (2023). AI in educational technology. Preprints. https://doi.org/10.20944/preprints202311.0106.v1
Zidaru, T., Morrow, E. M., & Stockley, R. (2021). Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice. Health Expectations, 24(4), 1072-1124. https://doi.org/10.1111/hex.13299
Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747