Development and Threats of Artificial Intelligence in Industry and Workforce

Authors

  • Francisca Romana Nanik Alfiani Universitas Borobudur, North Jakarta, DKI Jakarta

DOI:

https://doi.org/10.46799/ijssr.v4i03.756

Keywords:

Development and Threat, Artificial Intelligence, Industry, Workforce

Abstract

The abstract explores the transformative impact of Artificial Intelligence (AI) on global industries, highlighting its role in enhancing efficiency and innovation while also posing challenges such as job automation, skills gaps, data misuse, and ethical concerns. Drawing from the Global Risk Report 2024 and recent regulatory actions by the European Union and Indonesia, the abstract discusses the pressing need for AI governance. However, it lacks specificity in articulating the study's objectives and scope, and could benefit from providing concrete examples or statistics to support its claims. A clearer organizational structure and citation of relevant sources would enhance the coherence and credibility of the abstract. Furthermore, while emphasizing the importance of human control over AI, the abstract could offer a more nuanced conclusion that underscores the significance of the study's findings in achieving this goal.

References

Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1). https://doi.org/10.1016/J.JOITMC.2024.100218

Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44. https://doi.org/10.1016/J.JOBE.2021.103299

Addanki, M., Patra, P., & Kandra, P. (2022). Recent advances and applications of artificial intelligence and related technologies in the food industry. Applied Food Research, 2(2). https://doi.org/10.1016/J.AFRES.2022.100126

Alenizi, F. A., Abbasi, S., Hussein Mohammed, A., & Masoud Rahmani, A. (2023). The artificial intelligence technologies in Industry 4.0: A taxonomy, approaches, and future directions. Computers and Industrial Engineering, 185. https://doi.org/10.1016/J.CIE.2023.109662

Bangkara, R. P., & Mimba, N. (2016). Pengaruh perceived usefulness dan perceived ease of use pada minat penggunaan internet banking dengan attitude toward using sebagai variabel intervening. E-Jurnal Akuntansi Universitas Udayana, 16(3), 2408–2434.

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., & Filar, B. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. ArXiv Preprint ArXiv:1802.07228.

Chui, M., Yee, L., Hall, B., & Singla, A. (2023). The state of AI in 2023: Generative AI’s breakout year.

Druckman, A., & Mair, S. (2019). Wellbeing, Care and Robots—Prospects for good work in the health and social care sector. CUSP Work. Pap., 21.

Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024). Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240. https://doi.org/10.1016/J.ESWA.2023.122442

Hasani, N., Farhadi, F., Morris, M. A., Nikpanah, M., Rhamim, A., Xu, Y., Pariser, A., Collins, M. T., Summers, R. M., Jones, E., Siegel, E., & Saboury, B. (2022). Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities. PET Clinics, 17(1), 13–29. https://doi.org/10.1016/J.CPET.2021.09.009

He, J., Feng, W., Min, Y., Yi, J., Tang, K., Li, S., Zhang, J., Chen, K., Zhou, W., & Xie, X. (2023). Control risk for potential misuse of artificial intelligence in science. ArXiv Preprint ArXiv:2312.06632.

Hossain, M. A., Agnihotri, R., Rushan, M. R. I., Rahman, M. S., & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption, and firms’ competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240–255. https://doi.org/10.1016/J.INDMARMAN.2022.08.017

LI, Y. (2023). Relationship between perceived threat of artificial intelligence and turnover intention in luxury hotels. Heliyon, 9(8). https://doi.org/10.1016/J.HELIYON.2023.E18520

Liang, C.-J., Le, T.-H., Ham, Y., Mantha, B. R. K., Cheng, M. H., & Lin, J. J. (2024). Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Automation in Construction, 162, 105369. https://doi.org/10.1016/J.AUTCON.2024.105369

Mahalakshmi, V., Kulkarni, N., Pradeep Kumar, K. V., Suresh Kumar, K., Nidhi Sree, D., & Durga, S. (2022). The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Materials Today: Proceedings, 56, 2252–2255. https://doi.org/10.1016/J.MATPR.2021.11.577

Marr, B. (2019). Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.

Mpatziakas, A., Drosou, A., Papadopoulos, S., & Tzovaras, D. (2022). IoT threat mitigation engine empowered by artificial intelligence multi-objective optimization. Journal of Network and Computer Applications, 203. https://doi.org/10.1016/J.JNCA.2022.103398

Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., Ma, X., & Pathan, M. S. (2024). Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration. Journal of King Saud University - Computer and Information Sciences, 36(2). https://doi.org/10.1016/J.JKSUCI.2024.101939

Ofosu-Ampong, K. (2024). Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions. Telematics and Informatics Reports, 14. https://doi.org/10.1016/J.TELER.2024.100127

Pantano, E., Marikyan, D., & Papagiannidis, S. (2024). The dark side of artificial intelligence for industrial marketing management: Threats and risks of AI adoption. Industrial Marketing Management, 116, A1–A3. https://doi.org/10.1016/J.INDMARMAN.2023.11.008

Park, J. J. (2024). Unlocking training transfer in the age of artificial intelligence. Business Horizons. https://doi.org/10.1016/J.BUSHOR.2024.02.002

Permana, A. A., Darmawan, R., Saputri, F. R., Harto, B., Al-Hakim, R. R., Wijayanti, R. R., Safii, M., Pasaribu, J. S., & Rukmana, A. Y. (2023). Artificial Intelligence Marketing. Padang: Global Eksekutif Teknologi.

Sadeghi R., K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180. https://doi.org/10.1016/J.DSS.2024.114194

Schluse, M., Priggemeyer, M., Atorf, L., & Rossmann, J. (2018). Experimentable digital twins—Streamlining simulation-based systems engineering for industry 4.0. IEEE Transactions on Industrial Informatics, 14(4), 1722–1731.

Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)-enabled malware and intrusion detection. Journal of Industrial Information Integration, 36. https://doi.org/10.1016/J.JII.2023.100520

Sharma, P., Shah, J., & Patel, R. (2022). Artificial intelligence framework for MSME sectors with focus on design and manufacturing industries. Materials Today: Proceedings, 62(P13), 6962–6966. https://doi.org/10.1016/J.MATPR.2021.12.360

Spilseth, B., McKnight, C. D., Li, M. D., Park, C. J., Fried, J. G., Yi, P. H., Brian, J. M., Lehman, C. D., Wang, X. J., Phalke, V., Pakkal, M., Baruah, D., Khine, P. P., & Fajardo, L. L. (2022). AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence. Academic Radiology, 29(1), 119–128. https://doi.org/10.1016/J.ACRA.2021.08.002

Sudarsono, B., Tentama, F., & Ghozali, F. A. (2022). Employability Analysis of Students in Yogyakarta: Confirmatory Factor Analysis. Al-Ishlah: Jurnal Pendidikan, 14(2), 1451–1462.

Sugiyono, S. (2017). Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta. Procrastination And Task Avoidance: Theory, Research and Treatment. New York: Plenum Press, Yudistira P, Chandra.

Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., Rao, Y., Bednar, J. A., Tan, A., Wang, J., Purushotham, S., Gill, T. E., Chastang, J., Howard, D., Holt, B., … John, A. (2022). A review of Earth Artificial Intelligence. Computers & Geosciences, 159, 105034. https://doi.org/10.1016/j.cageo.2022.105034

Supriyanto, E. E., Warsono, H., & Herawati, A. R. (2021). Literature Study on the Use of Big Data and Artificial Intelligence in Policy Making in Indonesia. Administratio, 12(2), 139–153.

Thapa, A., Nishad, S., Biswas, D., & Roy, S. (2023). A comprehensive review on artificial intelligence assisted technologies in food industry. Food Bioscience, 56. https://doi.org/10.1016/J.FBIO.2023.103231

Trzaska, R., & Sus, A. (2023). Industry 4.0 business strategic risks based on the scalability 4.0 concept. Artificial Intelligence area. Procedia Computer Science, 225, 3255–3264. https://doi.org/10.1016/J.PROCS.2023.10.319

Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008.

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Published

2024-03-28