SECURITY AND DATA PROTECTION IN ARTIFICIAL INTELLIGENCE

Marko Marković; Dragan SOLEŠA

doi:10.5937/jpmnt13-58872

Marko Marković Univerzitet Privredna akademija u Novom Sadu, Fakultet za primenjeni menadžment, ekonomiju i finansije, Beograd, Srbija https://orcid.org/0009-0002-6449-6589
Dragan SOLEŠA Univerzitet Privredna akademija u Novom Sadu, Fakultet za ekonomiju i inženjerski menadžment, Novi Sad, Srbija

DOI: https://doi.org/10.5937/jpmnt13-58872

Sažetak

The development of artificial intelligence contributes to the digital transformation of the entire society, improving numerous processes, automation and better decision-making. In addition to the many opportunities it offers, it also brings with it great risks in data security. The problem is further complicated by unauthorized access to data processed by artificial intelligence models, data theft, ethical dilemmas and lack of algorithm transparency. This paper analyzes artificial intelligence through different fields and the problems it can cause. A special emphasis is on the research into user attitudes, which shows us what kind of knowledge respondents have about artificial intelligence and all the risks it can cause. The results help us to see what the biggest problems regarding the use of such models are. The work provides guidelines for minimizing risks and creating problems, while dictating the trend for responsible use of artificial intelligence for legally correct purposes.

Reference

Brandtzaeg, P. B., & Følstad, A. (2017). Why do people use chatbots? Internet Science, 377–392. https://doi.org/10.1007/978-3-319-70284-1_30

Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1–38. https://doi.org/10.1145/3502289

Castelfranchi, C. (2013). Alan Turing’s “Computing Machinery and Intelligence”. Topoi, 32(2), 193–199. https://doi.org/10.1007/s11245-013-9182

Danielsson, J., Macrae, R., & Uthemann, A. (2019). Artificial intelligence and systemic risk. SSRN Electronic Journal, 1–9. https://doi.org/10.2139/ssrn.3410948

Diligenski, A., Prlja, D., & Cerović, D. (2018). Pravo zaštite podataka. Institut za uporedno pravo.

https://www.rts.rs/magazin/tehnologija/5208016/cetbot-zanimanja-vestacka-inteligencija-radna-mesta.html (29.01.2025)

Đurica, N., & Soleša, D. (2017). Percepcija i stavovi studenata prema obrazovanju na daljinu. Ekonomija: teorija i praksa, 10(3), 1–15. https://doi.org/10.5937/etp1703001D

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4), 403–418. https://doi.org/10.1007/978-3-319-69623-2_15

Kamaruddin, S., Mohammad, A. M., Mohd Saufi, N. N., Wan Rosli, W. R., Othman, M. B., & Hamin, Z. (2023). Compliance to GDPR data protection and privacy in artificial intelligence technology: Legal and ethical ramifications in Malaysia. 2023 International Conference on Digital Transformation (ICDT), Greater Noida, India, 11–12 May 2023, 11–12. https://doi.org/10.1109/ICDT57929.2023.10150615

Kandolo, W. (2024). Ensuring AI data access control in RDBMS: A comprehensive review. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8400–8407.

Martinelli, F., Marulli, F., Mercaldo, F., Marrone, S., & Santone, A. (2020). Enhanced privacy and data protection using natural language processing and artificial intelligence. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19–24 July 2020, 1–8. https://doi.org/10.1109/IJCNN48605.2020.9206801

Mitrou, L. (2018). Data protection, artificial intelligence and cognitive services: Is the General Data Protection Regulation (GDPR) “artificial intelligence-proof”? SSRN, 1–10. https://doi.org/10.2139/ssrn.3386914

Niskanen, T., Sipola, T., & Väänänen, O. (2023). Latest trends in artificial intelligence technology: A scoping review. JAMK University of Applied Sciences, 1–26. https://doi.org/10.48550/arXiv.2305.04532

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., et al. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686

Sartor, G., & Lagioia, F. (2020). The impact of the General Data Protection Regulation (GDPR) on artificial intelligence. European Parliament, Brussels. https://doi.org/10.2861/293

Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), 1310–1321. https://doi.org/10.1145/2810103.2813687

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099

Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2024). Data-centric artificial intelligence: A survey. ACM Computing Surveys. https://doi.org/10.1145/3711118

Zheng, X., Zhu, M., Li, Q., Chen, C., Tan, Y., & Hide. (2018). FinBrain: When finance meets AI 2.0. arXiv, 1–12. https://doi.org/10.48550/arXiv.1808.08497