Technological, economic, social, ethical and legal factors key to the development of novel AI-based technologies
DOI:
https://doi.org/10.34767/SIMIS.2025.03.07Keywords:
Computer science, law, artificial intelligence, machine learning, technologies, market applications.Abstract
In light of the declarations of key AI economies and experts (USA, China) to accelerate AI adoption, including in the area of socalled home AI, this article discusses the factors contributing to the broader application of artificial intelligence (AI) in improving existing and developing entirely new market technologies. This applies particularly to automating routine tasks to increase efficiency, reduce human error, and improve access to products and services, especially personalized ones. Ethical issues are addressed, particularly those related to algorithmic bias, transparency of decision-making, and accountability for these decisions. The article concludes by highlighting the multifactorial reasons for the potential success of implementing new AI solutions, including consideration of sustainability and interdisciplinary collaboration and regulatory frameworks to ensure responsible and effective integration of AI with existing solutions.
References
Witkowski A., Wodecki A. An exploration of the applications, challenges, and success factors in AIdriven product development and management. Foundations of Management, 2024, 16(1).
Lada S., Chekima B., Karim M.R.A., Fabeil N.F., Ayub M.S., Amirul S.M., Zaki, H.O. Determining factors related to artificial intelligence (AI) adoption among Malaysia's small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 2023, 9(4), 100144.
Westenberger J., Schuler K., Schlegel D. Failure of AI projects: understanding the critical factors. Procedia computer science, 2022, 196, 69-76.
Wan, Z. Text Classification: A Perspective of Deep Learning Methods. arXiv 2023, arXiv:2309.13761. 5. Rojszczak, M. Prawne aspekty systemów sztucznej inteligencji - zarys problemu. Sztuczna inteligencja, blockchain, cyberbezpieczeństwo oraz dane osobowe. Wydawnictwo C.H Beck 2019, s. 1-22.
Wierczyński, G., Wiewiórowski, W.R. Prawne aspekty informatyki, (w:) Informatyka ekonomiczna: teoria i zastosowania, red. S. Wrycza, J. Maślankowski, Wydawnictwo Naukowe PWN, Warszawa 2019.
Wiewiórowski W. Kwanty informacji o osobie. Prawne aspekty przetwarzania danych o osobach i „obiektach” pochodzących z rozproszonych zbiorów [w:] P., Z. Rau, M. Wągrowski [red.:] Nowoczesne systemy łączności i transmisji danych na rzecz bezpieczeństwa. Szanse i zagrożenia, LEX Wolters Kluwer, Warsaw 2013, p. 8.
Simmons R. Big Data, Machine Judges, and the Legitimacy of the Criminal Justice System. SSRN, 2018, 52 (2), 1067-1118.
Wiewiórowski W.R. Profilowanie osób na podstawie ogólnodostępnych danych [w:] A. Mednis [red.:] Prywatność a ekonomia. Ochrona danych osobowych w obrocie gospodarczym, Wyd. WPiA Uniwersytetu Warszawskiego, Warszawa 2013, s. 16.
Macko, M., Szczepański, Z., Mikołajewski, D., Mikołajewska, E., Listopadzki, S. The Method of Artificial Organs Fabrication Based on Reverse Engineering in Medicine. In: Rusiński, E., Pietrusiak, D. (eds) Proceedings of the 13th International Scientific Conference . RESRB 2016. Notes in Mechanical Engineering. Springer, Cham 2017.
Wiewiórowski W.R. Prawna ochrona danych biometrycznych w systemach teleinformatycznych pracodawcy. Cele przetwarzania a zakres ochrony [w:] A. Nerka, T. Wyka [red.:] Ochrona danych osobowych podmiotów objętych prawem pracy i prawem ubezpieczeń społecznych. Stan obecny i perspektywy zmian, Wolters Kluwer Polska, Warszawa 2012, s. 10.
Gryz, J., Rojszczak M. Black box algorithms and the rights of individuals: No easy solution to the"explainability" problem. Internet Policy Review 10 (2), 1-24.
Węgrzyn-Wolska, K., Rojek, I., Dostatni, E., Mikołajewski, D., Pawłowski, L. Modern approach to sustainable production in the context of Industry 4.0. Bulletin Of The Polish Academy Of Sciences Technical Scienes 2022, 70(6), e143828.
Veziroğlu, M., Veziroğlu, E., Bucak, İ.Ö. Performance Comparison between Naive Bayes and Machine Learning Algorithms for News Classification. In Bayesian Inference—Recent Trends; IntechOpen: London, UK, 2024.
Łukaniszyn, M., Majka, Ł., Grochowicz, B., Mikołajewski, D., Kawala-Sterniuk, A. Digital Twins Generated by Artificial Intelligence in Personalized Healthcare. Appl. Sci. 2024, 14, 9404.
Tabany, M.; Gueffal, M. Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning Model. JAIT 2024, 15, 49–58.
Liu C.F., Huang C.C., Wang J.J., Kuo, K.M., Chen, C.J. The critical factors affecting the deployment and scaling of healthcare AI: viewpoint from an experienced medical center. Healthcare 2021, 9, 6, 685.
Mikołajewska, E., Mikołajewski, D. Neurorehabilitacja XXI wieku. Techniki teleinformatyczne. Impuls, Kraków 2011.
Ashley, K. A Brief History of the changing roles of case prediction in AI and Law: Law in context Socio-Legal Journal, 2019, 36 (1), 93-112.
Rojek, I., Kotlarz, P., Kozielski, M., Jagodziński, M., Królikowski, Z. Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine. Electronics 2024, 13, 272.
Bhilare, P., Parab, N., Soni, N., Thakur, B. Predicting outcome of judicial cases and analysis using machine learning. International Research Journal in Engineering Technology, 2019, 6, 326-330.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.