Received:
2021-12-15 | Accepted:
2022-02-24 | Published:
2022-03-30
Title
Transition towards the artificial intelligence via re-engineering of digital platforms: comparing European Member States
Abstract
t. The present study is directed at comparing the performances of the 27 European Member States in the period 2012 – 2021 with the choice of assessing their improvement in the transition toward Artificial Intelligence. The study, opening from an analysis of the key factors with strategic rate in the countries’ transition, is centered on society, law, physiology, and trust impacts. The valuation of the effects was evaluated by seeing forty - two official European Commission rapports in order to create a composite analysis and assessment of the state of the transition in Europe. The study pointed at understanding the degree of transition related to 27 European Member States in the realization of AI targets. The outcomes of the study highpoint that the transition, evaluated with the support of the composite analysis, is current with a different degree between the Member State and only some elements could be evaluated as suitable in the transition toward an artificial intelligence system in the Europe.
Keywords
Artificial Intelligence (AI), European Member States, digital platforms, trust
JEL classifications
L20
, L26
URI
http://jssidoi.org/jesi/article/953
DOI
Pages
350-368
Funding
As a part of the Research Program V: ALERE – Università della Campania Luigi Vanvitelli, Italy
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
References
Adams, S., Arel, I., Bach, J., et al. (2012). Mapping the landscape of human-level artificial general intelligence. AI Magazine, 33(1), 25–42. https://doi.org/10.1609/aimag.v33i1.2322
Search via ReFindit
Agliata, R., Marino, A., Mollo, L., & Pariso, P. (2020). Historic building energy audit and retrofit simulation with hemp-lime plaster—A case study. Sustainability, 12(11), 4620. https://doi.org/10.3390/su12114620
Search via ReFindit
Andrejczuk, E., Bistaffa, F., Blum, C., Rodríguez-Aguilar, J. A., & Sierra, C. (2019). Synergistic team composition: A computational approach to foster diversity in teams. Knowledge-Based Systems, 182, 104799. https://doi.org/10.1016/j.knosys.2019.06.007
Search via ReFindit
Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE computational intelligence magazine, 5(4), 13-18. https://doi.org/10.1109/MCI.2010.938364
Search via ReFindit
Armstrong, S., Sotala, K., & Ó’hÉigeartaigh, S. (2014). The errors, insights and lessons of famous AI predictions – and what they mean for the future. Journal of Experimental and Theoretical Artificial Intelligence, 26(3), 317–342. Special issue ‘Risks of General Artificial Intelligence’, ed. V. Müller. https://doi.org/10.1080/0952813X.2014.895105
Search via ReFindit
Asif, M., Jajja, M. S. S., & Searcy, C. (2019). Social compliance standards: Re-evaluating the buyer and supplier perspectives. Journal of Cleaner Production, 227, 457-471. https://doi.org/10.1016/j.jclepro.2019.04.157
Search via ReFindit
Blažič, S., & Škrjanc, I. (2019). Incremental fuzzy c-regression clustering from streaming data for local-model-network identification. IEEE Transactions on Fuzzy Systems, 28(4), 758-767. https://doi.org/10.1109/TFUZZ.2019.2916036
Search via ReFindit
Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225
Search via ReFindit
Capone, V., Marino, L., & Donizzetti, A. R. (2020). The English Version of the Health Profession Communication Collective Efficacy Scale (HPCCE Scale) by Capone and Petrillo, 2012. European Journal of Investigation in Health. Psychology and Education, 10(4), 1065-1079. https://doi.org/10.3390/ejihpe10040075
Search via ReFindit
Chen, J., Abbod, M., & Shieh, J. S. (2019). Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review. Sensors, 19(18), 3897. https://doi.org/10.3390/s19183897
Search via ReFindit
Chia, A., Kern, M. L., & Neville, B. A. (2020). CSR for Happiness: Corporate determinants of societal happiness as social responsibility. Business Ethics: A European Review, 29(3), 422-437. https://doi.org/10.1111/beer.12274
Search via ReFindit
Crane, A., Matten, D., Glozer, S., & Spence, L. (2019). Business ethics: Managing corporate citizenship and sustainability in the age of globalization. Oxford University Press, USA.
Search via ReFindit
Csillag, S. (2019). Ethical dilemmas and moral muteness in the HRM profession. Society and Economy, 41(1), 125-144. https://doi.org/10.1556/204.2019.41.1.8
Search via ReFindit
European Commission (2020) (last access June 2021) https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/excellence-trust-artificial-intelligence_it
Search via ReFindit
European Parliament (2020) (last access june 2021) https://www.europarl.europa.eu/news/it/headlines/society/20201015STO89417/regolamento-sull-intelligenza-artificiale-cosa-vuole-il-parlamento-europeo
Search via ReFindit
Freeman, R. E., Parmar, B. L., & Martin, K. (2020). Business and ethics (pp. 129-140). Columbia University Press.
Search via ReFindit
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459. https://doi.org/10.1038/nature14541
Search via ReFindit
Gindis, D., Veldman, J., & Willmott, H. (2020). Convergent and divergent trajectories of corporate governance. Competition & Change, 24(5), 399-407. https://doi.org/10.1177/1024529420944017
Search via ReFindit
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. https://doi.org/10.1016/j.ijme.2019.100330
Search via ReFindit
Greenwood, M.R. (2002) Ethics and HRM: A Review and Conceptual Analysis. Journal of Business Ethics, 36, 261–278. https://doi.org/10.1023/A:1014090411946
Search via ReFindit
Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC press.
Search via ReFindit
Legg S & Hunt M. (2007). A collection of definition of intelligence, in “Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms”. Edited by Goertzel Ben & Wang Pei, Proceeding of the AGI Workshop 2006, IOS press, pp. 17 – 23.
Search via ReFindit
Marino A. & Pariso P. (2021d). The global macroeconomic impacts of Covid-19: four European scenarios. Academy of Strategic Management Journal, 20(2), 1-21. https://www.abacademies.org/articles/the-global-macroeconomic-impacts-of-covid19-four-european-scenarios-10408.html
Search via ReFindit
Marino, A., & Pariso, P. (2021c). Digital government platforms: issues and actions in Europe during pandemic time. Entrepreneurship and Sustainability Issues, 9(1), 462. https://doi.org/10.9770/jesi.2021.9.1(29)
Search via ReFindit
Marino, A., Pariso, P. (2021b). Digital economy: technological, organizational and cultural contexts for the development of cooperation in Europe. Entrepreneurship and Sustainability Issues, 9(2), 363-383. https://doi.org/10.9770/jesi.2021.9.2(24)
Search via ReFindit
Medelyan, O., Milne, D., Legg, C., & Witten, I. H. (2009). Mining meaning from Wikipedia. International Journal of Human-Computer Studies, 67(9), 716-754. https://doi.org/10.1016/j.ijhcs.2009.05.004
Search via ReFindit
Mitchell, R., Michalski, J., & Carbonell, T. (2013). An artificial intelligence approach. Berlin: Springer. https://doi.org/10.1007/978-3-662-12405-5
Search via ReFindit
Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555-572). Springer, Cham. https://doi.org/10.1080/0952813X.2014.895105
Search via ReFindit
Navigli, R., & Ponzetto, S. P. (2012). BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial intelligence, 193, 217-250. https://doi.org/10.1016/j.artint.2012.07.001
Search via ReFindit
Nilsson, N. J. (2014). Principles of artificial intelligence. Morgan Kaufmann.
Search via ReFindit
Pariso, P., & Marino, A. (2020). From digital divide to e-government: re-engineering process and bureaucracy in public service delivery. Electronic Government, an International Journal, 16(3), 314-325. https://doi.org/10.1504/EG.2020.108495
Search via ReFindit
Phillips, R. A., & Margolis, J. D. (1999). Toward an ethics of organizations. Business Ethics Quarterly, 619-638. https://doi.org/10.2307/3857939
Search via ReFindit
Poff, D. C., & Michalos, A. C. (2019). Encyclopedia of Business and Professional Ethics. Springer
Search via ReFindit
Russell, S., Hauert, S., Altman, R., & Veloso, M. (2015). Ethics of artificial intelligence. Nature, 521(7553), 415-416. https://doi.org/10.1038/521415a
Search via ReFindit
Siarry, P., Sangaiah, A. K., Lin, Y.-B., Mao, S., & Ogiela, M. R. (2021). Guest Editorial: Special Section on Cognitive Big Data Science Over Intelligent IoT Networking Systems in Industrial Informatics. IEEE Transactions on Industrial Informatics, 17(3), 2112–2115. https://doi.org/10.1109/tii.2020.3024894
Search via ReFindit
Stemler, A., Perry, J. E., & Haugh, T. (2019). The Code of the Platform. Ga. L. Rev., 54, 605.
Search via ReFindit
Swanson R. and Chermack T., 2013. Theory building in applied disciplines. San Francisco, CA: Berrett-Koehler Publishers.
Search via ReFindit
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