حکمرانی هوش مصنوعی در دولت الکترونیک (چالش ها، فواید)
محورهای موضوعی : مدیریت دولتی
1 - استادیار گروه مهندسی صنایع ، واحد زنجان ، دانشگاه آزاد اسلامی ، زنجان ، ایران
کلید واژه: هوش مصنوعی, مدیریت منابع انسانی, دولت الکترونیک.,
چکیده مقاله :
علی رغم رشد روز افزون در حوزه هوش مصنوعی، بسیاری از مدیران سازمانی هنوز نتوانستهاند ارتباط خوبی با این فناوری برقرار کنند. سر در آوردن از هوش مصنوعی هم مثل هر تکنولوژی جدید دیگر که با کلی هیاهو و جنجال رسانهای همراه است، ممکن است گیجکننده باشد و حتی متخصصان هوش مصنوعی هم بهسختی میتوانند خود را با تحولات لحظهای این فناوری همراه کنند. تحقیق حاضر سعی بر آن دارد تا به روش تحلیل مضمون به بررسی جامعی از پشت پردهی تاثیر این فناوری مرموز و قدرتمند در هوشمند سازی مدیریت منابع انسانی بپردازد. بدین منظور یافته های جدیدترین مطالعات انجام شده در حوزه ی هوش مصنوعی با محوریت مدیریت منابع انسانی و کاربری دولت الکترونیک، از نشریات معتبر علمی- پژوهشی بین المللی مد نظر قرار گرفتند. پس از دسته بندی و کدگذاری داده های مذکور در نهایت روایت نظری تحقیق تدوین وارائه گرید. یافته های مذکور در پنج کد کلان شامل چالش های پیش روی هوش مصنوعی با نه مولفه، اخلاق حرفه ای با هفت مولفه ، نظام دولت الکترونیک با شش مولفه، نظام اداری با هفت مولفه، هوشمندسازی دولت ها با چهار مولفه احصا گردیدند.
Artificial Intelligence Governance in E-Government
(Challenges, Benefits)
Seyyed Kamran Yeganegi
Assistant Professor, Department of Industrial Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
Received 30 April 2024 | Accepted 12 June 2024
Extended Abstract
Despite the increasing growth in the field of artificial intelligence, many organizational managers have not yet been able to establish a good relationship with this technology. Getting your head around artificial intelligence, like any new technology that comes with a lot of hype and media controversy, can be confusing, and even experts in artificial intelligence can hardly keep up with the momentary developments of this technology.
The present research tries to comprehensively examine behind the curtain the impact of this mysterious and powerful technology in the intelligentization of human resources management using thematic analysis. For this purpose, the findings of the latest studies conducted in the field of artificial intelligence, focusing on human resource management and the use of e-government, from international scientific research publications were considered. After categorizing and coding the mentioned data, finally, the theoretical narrative of the research was compiled and presented.
The mentioned findings were counted in five major codes, including the challenges facing artificial intelligence with nine components, professional ethics with seven components, electronic government system with six components, administrative system with seven components, and the intelligentization of governments with four components in general, it should be noted that the advancement of artificial intelligence technology means a better life for everyone.
Artificial intelligence has vast potential and the responsibility for its proper implementation and execution, taking into account all the risks, is ours, but it requires intelligent performance by human resource managers in the field of human status and humanity because these robots may be intelligent but they are not smart. In this area, the relationship between understanding this phenomenon from the perspectives of Western and Islamic philosophy should be considered; From the perspective of Western philosophy, in the philosophy of mind, the last person to propose a two-dimensional human was Descartes (Lo, 2023) and then in the time of Hume, the definition of human changed and he took the metaphysical dimension from man and enclosed him in this physical human circle (NDUBISI, 2023).
They questioned the place of science and religion in human life (Kalkman, 2023); even in the field of actions and behavior, they said that all these emotions, feelings, and sensual perceptions are the result of mental and brain actions and reactions in man and they removed metaphysics and God from this equation in general (Scolari, 2023). Therefore, they limited the mind so much that they interpreted all human behaviors as the result of the sum of these actions and reactions.
Because a robot cannot do anything without being programmed by a human and even after receiving the latest programs, it cannot do many things and still the human factor is needed for training and solving work problems.
One of the priorities that managers and leaders of companies can do in dealing with the entry of machines into the workplace and the automation of work is to transfer experiences and retrain work skills from humans to robots. If, as a manager, you want to transfer experiences and skills to smart robots in the complex under your management, you must do this with your human resources as the focus.
The developments and advances of artificial intelligence will affect some industries and jobs more than others in the future, and we will witness the disappearance of several jobs shortly and the retraining and employment of numerous people who will be unemployed as a result of the disappearance of these jobs will become one of the great social challenges of the future.
In addition, middle managers and white-collar workers will also have to prepare themselves to work alongside robots and machines soon, so they must learn new skills. Accordingly, based on research such as Krasketal, 2023), (Chen & Das, 2023), (Said et al. 2023), (Saura et al. 2022), (Debrah et al. 2022), (Thakur, 2024), (Saxena & Khandelwal, 2022), (Kiyasseh et al. 2022), the best solution to reconcile artificial intelligence with human resources concerned about future demotion and unemployment is for artificial intelligence systems to "blend" with the human workforce, rather than replacing them and marginalizing them. Therefore, it must be accepted that the key to the success of artificial intelligence systems in practice is that, whether at the time of design, during implementation, or afterward, a tangible type of agency and influence must be defined for humans.
Humans must be given the opportunity and authority to take on some sensitive and important functions and provide human solutions to solve problems, and this will allow us to witness “responsible implementation of artificial intelligence programs by employees” in organizations.
Finally, we can assure employees and human resources concerned about the rapid advances in artificial intelligence systems that if special attention was paid to human and ethical issues when planning for the development of artificial intelligence, the lives and work of those who will be affected by the expansion of artificial intelligence were also taken into account, we can hope for the realization of the dream of ethical and entrepreneurial artificial intelligence more than ever before.
Keywords: Artificial Intelligence, Human Resources Management, Electronic Government.
Contribution of authors
All authors have participated in this research in equal proportion.
Ethical approval
Written informed consent was obtained from the individuals for their anonymized nformation to be published in this article.
Conflict of interest
No conflicts of interest are declared by the authors.
Jovari, Behnoush, Mohammadi Moghadam, Yousef. (2010). Joy and strategies for its implementation in universities. New Educational Thoughts, 17(1), 245-271. Doi: 10.22051/JONTOE.2021.26926.2723
Roshen, Seyed Aligholi, Yaghoubi, Noor Mohammad, Momeni, Amir Reza. (2010). Application of Artificial Intelligence in the Public Sector (A Meta-Synthesis Study). Quarterly Journal of the Iranian Management Sciences Association, 16(61), 117-145. https://journal.iams.ir/article_349.html
Zohri Bidgoli, Seyed Mohsen, Mohammadi Moghadam, Yousef, Jovari, Behnoush, Ghaibi, Parvaneh. (2010). The semantic system of discourse, its why and how in the wills of martyrs based on the theory of Laclau and Mouffe (Case study: the wills of martyrs of the Fars province police force). Quarterly Journal of Sacred Defense Studies, 6(4), 29-9. https://dorl.net/dor/20.1001.1.25883674.1399.6.4.1.6
Sazmand, Bahareh. (2018). Artificial Intelligence in the World (3) (People's Republic of China). Tehran, Iran: Research Center of the Islamic Consultative Assembly. https://sid.ir/paper/792606/fa
Futouhi Roudmoejni, Mahmoud. (2017). Research Article Writing Procedure, Tehran, Sokhan Publications, Third Edition, Sixteenth Edition.
Moghanlou, Amir Mohammad (2014), Factors Affecting the Implementation of Electronic Consultation on the Adoption of Electronic Procurement with the Mediating Role of Supervisory Policy in Zanjan Municipality, Master's Thesis, Islamic Azad University, Zanjan Branch, Supervisor: Seyyed Kamran Yegangi.
Ahmad, K., Abdelrazek, M., Arora, C., Bano, M., & Grundy, J. (2023). Requirements engineering for artificial intelligence systems: A systematic mapping study. Information and Software Technology, 107176. https://doi.org/10.1016/j.infsof.2023.107176
Akour, I., Alzyoud, M., Alquqa, E., Tariq, E., Alzboun, N., Al-Hawary, S & Alshurideh, M. (2024). Artificial intelligence and financial decisions: Empirical evidence from developing economies.International Journal of Data and Network Science, 8(1), 101-108. http://dx.doi.org/10.5267/j.ijdns.2023.10.013
Al-Besher, A., Kumar, K. (2022): Use of artificial intelligence to enhance e-government services. Meas. Sens. 24, 100484. https://www.sciencedirect.com/science/article/pii/S2665917422001180
Aleem, M., Sufyan, M., Ameer, I., & Mustak, M. (2023). Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda. Journal of business research, 154, 113303. https://doi.org/10.1016/j.jbusres.2022.113303
BAKKAL, A. K., & ALİMEN, N. (2023) .Modernization by Translation, Modernization in Translation: From Hace-i Evvel to Bot Poet–An INTRA Case. transLogos Translation Studies Journal, 5(2), 134-158. https://doi.org/10.29228/transLogos.51
Buzko, I., Dyachenko, Y., Petrova, M., Nenkov, N., Tuleninova, D., & Koeva, K. (2016). Artificial Intelligence technologies in human resource development. Computer modelling and new technologies, 20(2), 26-29. https://www.researchgate.net/publication/308031679_Artificial_Intelligence_technologies_in_human_resource_development
Chen, P. Y., & Das, P. (2023). AI Maintenance: A Robustness Perspective. Computer, 56(2), 48-56. http://dx.doi.org/10.1109/MC.2022.3218005
Cheng, W., Li, G., & Liu, S. (2020). Enlightenment of Human-Machine Cooperation on Human Resource Management in the Era of Artificial Intelligence. http://dx.doi.org/10.12677/MM.2020.101015
Debrah, C., Chan, A. P., & Darko, A. (2022). Artificial intelligence in green building. Automation in Construction, 137, 104192. https://doi.org/10.1016/j.autcon.2022.104192
Desouza, K. C. (2021). IBM Center for The Business of Government Artificial Intelligence in the Public Sector: A Maturity Model. www.businessofgovernment.org.
Gartner, S., & Krašna, M. (2023). Artificial intelligence in education-ethical framework. In 2023 12th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-7). IEEE. https://doi.org/10.1109/MECO58584.2023.10155012
Giudici, P., Centurelli, M., & Turchetta, S. (2024). Artificial Intelligence risk measurement. Expert Systems with Applications, 235, 121220. https://doi.org/10.1016/j.eswa.2023.121220
Grzybowski, A., Jin, K., & Wu, H. (2024). Challenges of artificial intelligence in medicine and dermatology. Clinics in Dermatology. https://doi.org/10.1016/j.clindermatol.2023.12.013
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, 122442. https://doi.org/10.1016/j.eswa.2023.122442
Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/j.bushor.2022.03.002
Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management. https://aisel.aisnet.org/iceb2018/91
Joyce, D. W., Kormilitzin, A., Smith, K. A., & Cipriani, A. (2023). Explainable artificial intelligence for mental health through transparency and interpretability for understandability. npj Digital Medicine, 6(1), 6. https://doi.org/10.1038/s41746-023-00751-9
Kalkman, M. L. (2023). Theosemiosis: An essay on consilience and the perennial philosophy. Sign Systems Studies, 51(2), 398-432. https://doi.org/10.12697/SSS.2023.51.2.10
Kiyasseh, D., Laca, J., Haque, T. F., Miles, B. J., Wagner, C., Donoho, D. A., & Hung, A. J. (2023). A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons. Communications Medicine, 3(1), 42. https://doi.org/10.1038/s43856-023-00263-3
Kraske, B. D., Saksena, A., Buczak, A. L., & Sunberg, Z. N. (2023). Explanation through Reward Model Reconciliation using POMDP Tree Search. arXiv preprint arXiv:2305.00931. https://doi.org/10.48550/arXiv.2305.00931
Laux, J., Wachter, S., & Mittelstadt, B. (2024). Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18(1), 3-32. https://doi.org/10.1111/rego.12512
Lo, M. (2023). Skepticism’s Pictures: Figuring Descartes’s Natural Philosophy. Penn State Press. https://www.amazon.com/Skepticisms-Pictures-Figuring-Descartess-Philosophy/dp/0271094826
Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2023). Artificial intelligence in government: Taking stock and moving forward. Social Science Computer Review, 41(1), 123-140. https://doi.org/10.1177/08944393211034087
Ndubisi, E. J. (2023). AGAINST HUME’S METAPHYSICAL NIHILISM. Journal of African Studies and Sustainable Development. https://acjol.org/index.php/jassd/article/view/3686
Neri, H., & Cozman, F. (2020). The role of experts in the public perception of risk of artificial intelligence. AI & SOCIETY, 35, 663-673. https://doi.org/10.1007/s00146-019-00924-9
Oladoyinbo, T. O., Olabanji, S. O., Olaniyi, O. O., Adebiyi, O. O., Okunleye, O. J., & Alao, A. I. (2024). Exploring the challenges of artificial intelligence in data integrity and its
influence on social dynamics. Asian Journal of Advanced Research and Reports, 18(2), 1-23. http://dx.doi.org/10.9734/AJARR/2024/v18i2601
Ramachandran, K. K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J. R. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51, 2327-2331. http://dx.doi.org/10.1016/j.matpr.2021.11.544
Rožman, M., Tominc, P., & Milfelner, B. (2023). Maximizing employee engagement through artificial intelligent organizational culture in the context of leadership and training of employees: Testing linear and non-linear relationships. Cogent Business & Management, 10(2), 2248732. DOI%3A%2010.1080/23311975.2023.2248732
Rudko, I., Bashirpour Bonab, A., & Bellini, F. (2021). Organizational structure and artificial intelligence. Modeling the intraorganizational response to the ai contingency. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2341-2364. https://doi.org/10.3390/jtaer16060129
Ryan, M. (2020). In AI we trust: ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26(5), 2749-2767. https://doi.org/10.1007/s11948-020-00228-y
Said, G., Azamat, K., Ravshan, S., & Bokhadir, A. (2023). Adapting Legal Systems to the Development of Artificial Intelligence: Solving the Global Problem of AI in Judicial Processes. International Journal of Cyber Law, 1(4). https://doi.org/10.59022/ijcl.49
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Assessing behavioral data science privacy issues in government artificial intelligence deployment. Government Information Quarterly, 39(4), 101679. https://doi.org/10.1016/j.giq.2022.101679
Saxena, N., & Khandelwal, A. R. (2022). Effectiveness of E‐HRM Tools Using the Functionalities of Artificial Intelligence During Remote Working in Lockdown Period. Impact
of Artificial Intelligence on Organizational Transformation, 387-397. http://dx.doi.org/10.1002/9781119710301.ch22
Scolari, P. (2023). Death of God, nihilism, human existence. Gabriel Marcel and Friedrich Nietzsche. REVISTA DIALECTUS, 28(1), 203-221. http://dx.doi.org/10.30611/2023n28id86630
Shaikh, F., Afshan, G., Anwar, R. S., Abbas, Z., & Chana, K. A. (2023). Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources, 61(4), 794-820. DOI:10.1111/1744-7941.12385
Shaukat, K., Iqbal, F., Alam, T. M., Aujla, G. K., Devnath, L., Khan, A. G., & Rubab, A. (2020). The impact of artificial intelligence and robotics on the future employment opportunities. Trends in Computer Science and Information Technology, 5(1), 050-054. https://doi.org/10.17352/tcsit.000022
Shreve, J. T., Khanani, S. A., & Haddad, T. C. (2022). Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations. American Society of Clinical Oncology Educational Book, 42, 842-851. https://doi.org/10.1200/edbk_350652
Thakur, R. (2024). Introduction to artificial intelligence and its importance in modern business management. In Leveraging AI and emotional intelligence in contemporary business organizations (pp. 133-165). IGI Global. DOI: 10.4018/979-8-3693-1902-4.ch009
Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83, 101174. https://psycnet.apa.org/doi/10.1016/j.cogsys.2023.101174
Tran, O., Le, T. D., & Hang, N. P. T. (2023). Impacts of human capital, the fourth industrial revolution, and institutional quality on unemployment: An empirical study at Asian countries. Journal of Eastern European and Central Asian Research (JEECAR), 10(2), 238-250. http://dx.doi.org/10.15549/jeecar.v10i2.1010
Walters, R., & Novak, M. (2021). Artificial Intelligence and Law. In Cyber Security, Artificial Intelligence, Data Protection & the Law (pp. 39-69). Singapore: Springer Singapore. https://dx.doi.org/10.2139/ssrn.3911699
Wang, Y., Fu, E. Y., Zhai, X., Yang, C., & Pei, F. (2024). Introduction of artificial Intelligence. In Intelligent Building Fire Safety and Smart Firefighting (pp. 65-97). Cham: Springer Nature Switzerland. https://www.springerprofessional.de/en/intelligent-building-fire-safety-and-smart-firefighting/26664236
Zhang, C., Zhu, W., Dai, J., Wu, Y., & Chen, X. (2023). Ethical impact of artificial intelligence in managerial accounting. International Journal of Accounting Information Systems, 49, 100619. https://dx.doi.org/10.2139/ssrn.4394217
Zhang, W & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, (2021). "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C). DOI: 10.1016/j.techsoc.2021.101675
Zhou, Q., Li, B., Han, L., & Jou, M. (2023). Talking to a bot or a wall? How catboats vs. human agents affect anticipated communication quality. Computers in Human Behavior, 143, 107674. http://dx.doi.org/10.1016/j.chb.2023.107674