Strategy for Applying Artificial Intelligence in State Institutions
Abstract
Artificial intelligence and its practical applications have become one of the topics that have taken up a wide scope in modern studies, and the scope of its study has expanded to include fields that were not known a decade ago, especially after its practical applications entered various aspects of the country, from military plans and mechanisms, advanced automation of the economic system, international trade applications, and the introduction of artificial intelligence. In improving and evaluating institutional performance in the country, accordingly, the research comes as an attempt to determine the strategy for applying artificial intelligence in government institutions to reach an institutional performance evaluation process based on impartiality and objectivity.
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References
Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans-and where they can't (yet). The McKinsey Quarterly, 1-12.
Diamond, R. (2016). The determination and Welfare Implications of US Workers’ Diverging Location Choices by Skill: 1980-2000. American Economic Review, 3(106), 479-524.
Riahi, Y., & Riahi, S. (2018). Big data and big data analytics: Concepts, types and technologies. International Journal of Research and Engineering, 5(9), 524-528.
Valle, D. (2019). Public value of e-government services through emerging technologies. International Journal of Public Sector Management, 32(2), 533.
Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4, pp. 1043-1171). Elsevier.
Allen, R. (2009). Eagles’ Pause: Technical Change, Capital Accumulation, and Inequality in the British Industrial Revolution. Explorations in Economics History, 46(4), 418-435.
Bakhi, H. (2017). The future of Skills: Employment in 2030: Report Prepared by Nesta and Oxford Martin School. Oxford. Retrieved from https://www.nesta.org.uk/sites/dfault/files/th_future_of_skills_employment_in 2030_0.pdf
Biggio, B. (2017). Poisoning Attacks Against Support Vector Machines. Proceedings of the 29th International Conference on Machine Learning. Cornell: Cornell University. Retrieved from https://arxiv.org/abs/1206.6389v1
Brynolfsso , E., & et.al. (2017). Artificial intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. Cambridge: National Bureau of Economic Research.
Crafts, N. (2010). The contribution of New Technology to Economic Growth: Lessons from Economic History. CARE Online Working Paper Series 01, Competitive advantage in Global Economy. Retrieved from https://warwick.ac.uk/foc/economics/research/centre/cage/manage.publications/0
Dhaher, O. (2019). The Right of Investor in Tax and Customs Exceptions Under Investment Laws of Kurdistan Region and Iraq: A Comparative Study. International Journal of Law, 5(6), 65-68.
Mahdi, E. (2011). Political Thinking and Strategic Analysis in the financial ministry. journal of Economics and Administrative Sciences, 17(63).
Mahdi, E. (2017). The impact of the characteristics of human resources information systems in the implementation of the strategic decision of the state: Applied research in the Iraqi Ministry of Oil. Journal of Kufa legal and political science, 30(1).
Mahdi, E. (2023). Strategic Activation of Management Information Systems to Eliminate Political Corruption in State Institutions: Applied research in the facilities of the Iraqi Ministry of Oil. QALAAI ZANIST JOURNAL, 3(8), 489-511.
Mahdi, I. (2022). The Role of the E-government System in the Political Reform of State Institutions: Applied research. Cihan University-Erbil journal of humanities and social sciences, 2(5), 43-52.
Massoudi, A., Fatah, S., & Jami, M. (2024). The Role of Artificial Intelligence Application in Strategic Marketing Decision-making Process. Cihan University-Erbil Journal of Humanities and Social Sciences, 8(1), 34-39.
Massoudi, A., & Birdawod, H. (2023). Applying Knowledge Management Processes to Improve Institutional Performance. Cihan University-Erbil Journal of Humanities and Social Sciences, 7(1), 1-10.
OECD. (2020). Recommendation of the Council on Artificial Intelligence. shorturl.at/kwHU9.
Osoba, O. (2017). An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence. Calif.: RAND Corporation, RR-1744-RC, 2017. Retrieved from https://www.rand.org/pubs/research_reports/RR1744.html
Ron, S. (2016). Big data and the transformation of public policy analysis. Journal of Political Analysis and Management, 35(3), 715–721.
Rotenberg, V. (2013). Moravec’s Paradox: Consideration in the Context of Two Brain Hemisphere Functions. The Journal for Neurocognitive Research, 55, 108-111.
Thomas, D. (2014). Big data in research on social policy. ournal of Policy Analysis and Management, 33(2), 544–547.
Varisk, M. (2018). Human Rights Outlook. Retrieved from www.maplecroft.com/portfolio/new-chains-set-spiral-over-next-asia-supply-two-decades-automation-consumes-job-human-rights-outlook
Wallace, S. (2017). The Age of Automation: Artificial Intelligence, Robotics and the Future of Low-Skilled Work. London: RSA Action and Research Centre. Retrieved from http://www.theresa.org/globalassets/pdf/report/rsa_the_age_of-automation-report.pdf
Yogesh, K. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 1-2. doi:https://doi.org/10.1016/j.ijinfomgt.2019.08.002
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