Evaluation of the Use of Artificial Intelligence In Public Administrations within the Framework of Country Practices and Basic Public Principles

Yıl 2024, , 1 - 32, 22.04.2024

Burçin Bozdoğanoğlu , İraz Haspolat , Ayşegül Yücel

https://doi.org/10.26745/ahbvuibfd.1424290

Cited By: 2

Öz

The benefits and challenges of artificial intelligence, one of the powerful technologies emerging with the digital transformation process, have initiated an intense debate. Artificial intelligence is basically defined as intelligent systems capable of thinking and learning. Artificial intelligence technology, which can actively contribute to economic growth and national development of each country, is nowadays examined as an umbrella concept covering various techniques such as deep learning, machine learning, natural language processing, speech recognition, computer vision, neural network modeling. The use of artificial intelligence in the public sector can occur in different areas such as health, transportation, security, decision support systems. The use of artificial intelligence in the public sector brings many benefits such as reducing the number of manpower-based tasks, analysing the collected data, cost savings, efficiency and reducing corruption. However, ensuring the security of the data collected by artificial intelligence, legal protection and the use of this technology in administrative decision-making processes are open to discussion in terms of the adequacy of the existing legal regulations in terms of overlapping with the basic principles of public governance. In this study, the use of artificial intelligence in different fields in the public sector will be analysed within the framework of some country examples and the compatibility of these usage processes with basic public values such as efficiency, accountability, transparency and equality will be evaluated.

Anahtar Kelimeler

Artificial intelligence, public administrations, transparency, accountability, digitalisation

Kamu İdarelerinde Yapay Zekâ Kullanımının Ülke Uygulamaları ve Temel Kamusal İlkeler Çerçevesinde Değerlendirilmesi

Öz

Dijital dönüşüm süreciyle birlikte ortaya çıkan güçlü teknolojilerden birisi olan yapay zekaya ilişkin faydalar ve güçlükler beraberinde yoğun bir tartışma başlatmıştır. Yapay zekâ, temel olarak düşünme ve öğrenme yeteneğine sahip akıllı sistemler olarak tanımlanmaktadır. Ekonomik büyümeye ve her ülkenin ulusal gelişimine aktif katkı sağlayabilecek olan yapay zekâ teknolojisi, günümüzde derin öğrenme, makine öğrenimi, doğal dil işleme, konuşma tanıma, bilgisayar görüşü, nöral ağ modeli gibi çeşitli teknikleri kapsayan bir şemsiye kavram olarak incelenmektedir. Yapay zekanın kamu sektöründe kullanımı ise sağlık, ulaşım, güvenlik, karar destek sistemleri gibi farklı alanlarda ortaya çıkabilmektedir. Yapay zekanın kamu sektöründe kullanımı, insan gücüne dayalı olarak yapılan işlerin sayısını azaltarak bunları toplanan verilerin analizi üzerinden gerçekleştirmekte, maliyet tasarrufu sağlayarak verimlilik ve yolsuzluğun azaltılması gibi birçok faydayı beraberinde getirmektedir. Ancak yapay zekâ tarafından toplanan verilerin güvenliğinin sağlanması, hukuki açıdan korunması ve idari karar verme süreçlerinde bu teknolojinin kullanımı kamusal yönetişimin temel ilkeleriyle örtüşmesi noktasında mevcut yasal düzenlemelerin yeterliliği açısından tartışmaya açıktır. Çalışmada yapay zekanın kamu sektöründe farklı alanlarda kullanımı bazı ülke örnekleri çerçevesinde incelenerek, bu kullanım süreçlerinin etkinlik, hesap verebilirlik, şeffaflık, eşitlik gibi temel kamusal değerlerle uyumu değerlendirilecektir.

Anahtar Kelimeler

Yapay zekâ, kamu idareleri, şeffaflık, hesapverebilirlik, dijitalleşme

Kaynakça

• Anastasopoulos, L.J. ve A.B. Whitford (2019). “Machine learning for public administration research, with application to organizational reputation”, Journal of Public Administration Research and Theory, Vol. 29/3, 491-510.
• Bansak, K., J. Ferwerda, J. Hainmueller, A. Dillon, D. Hangartner, D. Lawrence and J. Weinstein (2018). “Improving refugee integration through data-driven algorithmic assignment”. Science. Vol. 359/6373, 325-329.
• BBC News (2019), “Could an algorithm help prevent murders?”. 24 June, www.bbc.com/news/stories-48718948.
• Berryhill J, Heang K K, Clogher R. ve Bride Mc. K. (2019). “Hello, World; Artificial Intelligence and Its Use in the Public Sector”. OECD Working Papers on Public Governance, No. 36, OECD Publishing, Paris, https://doi.org/10.1787/726fd39d-en.
• Bini, S. (2018). “Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care?”. The Journal of Arthroplasty, 33(8), 2358-2361. Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. https://doi.org/10.1063/1.1144830
• Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press
• Campbell, C. (2019). “How China Is Using Social Credit Scores to Reward and Punish Its Citizens”. July 2019, Time, https://time.com/collection/davos-2019/5502592/china-social-credit-score/, (03.07.2023)
• Carney, M. (2020). “Leave no dark corner”. Foreign Correspondent. 31.07.2020. https://www.abc.net.au/news/2018-09-18/china-social-credit-a-model-citizen-in-a-digital-dictatorship/10200278 , (21.07.2023)
• Carter, S. ve Nielsen, M. (2017). “Using Artificial Intelligence to Augment Human İntelligence”, Distill, 4 December, https://distill.pub/2017/aia.
• Chignard, S. ve Penicaud, S. (2019). “With great comes great responsibility: keeping public sector algorithms accountable”. 11.06.2019, Etalab Working Paper on algoritmic accountability. https://github.com/etalab/algorithmes-publics/blob/master/20190611_WorkingPaper_PSAAccountability_Etalab.pdf , (22.05.2023)
• CSSF (2018). Artificial Intelligence: Opportunities, Risks and Recommendations for the Financial Sector, Luxembourg, Commission de Surveillance du Secteur Financier, https://www.cssf.lu/wp-content/uploads/files/Publications/Rapports_ponctuels/CSSF_White_Paper_Artificial_Intelligence_201218.pdf
• Cuau, C. (2019). “Applying artifcial intelligence to citizen participation: the Youth4Climate case study”. Citizenlab Platform. https://www.citizenlab.co/blog/civic-engagement/youth-for-climate-case-study/ , (05.03.2023)
• Data.europe.eu (2023). “Enhancing transparency through open data”. 08.04.2019, Le portail officiel des donées européennes, https://data.europa.eu/fr/news-events/news/enhancing-transparency-through-open-data (12.03.2023)
• Dencik, L., Hintz, A., Redden, J. ve Warne, H. (2018). “Data Scores as Governance: Investigating uses of citizen scoring in public services". Data Justice Lab, Cardiff University, UK, https://datajustice.files.wordpress.com/2018/12/data-scores-as-governance-project-report2.pdf ,(21.06.2023)
• Eggers, W.D., Schatsky, D. ve Viechnicki, P. (2017). AI-Augmented Government: Using Cognitive Technologies to Redesign Public Sector Work, New York, Deloitte University Press, https://www2.deloitte.com/content/dam/insights/us/articles/3832_AI-augmented-government/DUP_AI-augmented-government.pdf ,(25.07.2023).
• European Commission (2019). A Definition of AI: Main Capabilities and Disciplines, definition developed for the purpose of the AI HLEG’s deliverables, Independent High- Level Group on Artificial Intelligence set up by the European Commission, Brussels, https://ec.europa.eu/futurium/en/system/files/ged/ai_hleg_definition_of_ai_18_december_1.pdf ,(22.05.2023)
• EUR-LEX (2021). Proposal for a Regulation of the European Parliament and of the Council Laying down Harmonised Rules on Artificial Intelligence and Amending Certain Union Legislative Acts. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206 , (20.05.2023)
• Frank, M.R., D. Wang, M. Cebrian ve I. Rahwan (2019). “The evolution of citation graphs in artificial intelligence research”, Nature Machine Intelligence, Vol. 1, 79- 85, www.nature.com/articles/s42256-019-0024-5 , (08.04.2023).
• GovInsider (2017). “Singapore trials AI to predict bus crashes”, https://govinsider.asia/intl-en/article/singapore-trials-ai-to-predict-bus-crashes , (01.07.2023).
• Hao, K. (2019). “AI is sending people to jail-and getting it wrong”. MIT Technology Review, 21.01.2019. https://www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/ , (21.07.2023).
• Herd, P. ve Moynihan, D.P. (2018). Administrative Burden: Policymaking by Other Means. Russell Sage Foundation, New York, www.jstor.org/stable/10.7758/9781610448789.
• Husnjak, S., Perakovic D., ve Jovovic I. (2014). “Possibilities of using speech recognition systems of smart terminal devices in traffic environment,”, Procedia Engineering, Vol. 69, 778-787.
• Lin, G., Brent, S. ve York, J. (2003). “Amazon.com Recommendations: Item-to Item Collobrative Filtering”. Industry Report. https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf , (28.06.2023).
• Madelin, R. ve Ringrose, D. (2016). “Opportunity now: Europe’s mission to innovate”. European Commission, https://www.oecd.org/education/ceri/GEIS2016-MadelinReport-Full.pdf ,(03.07.2023)
• Mateos-Garcia, J. (2017), “Algorithmic fallibility and economic organisation”. Nesta (blog), 10 May, https://osf.io/xuvf9/download/?format=pdf , (02.07.2023)
• Moneycontrol News (2019), “Gartner debunks five Artificial Intelligence misconceptions”. Moneycontrol, 15 February, www.moneycontrol.com/news/business/companies/gartner-debunks-five-artificial- intelligence-misconceptions-3545891.html
• NCSC (2019). Intelligent Security Tools. https://www.ncsc.gov.uk/collection/intelligent-security-tools , (23.07.2023)
• Nielsen, M. (2015). “Neural networks and deep learning”, Vol. 25, USA: Determination press, OECD (2015). Data-Driven Innovation: Big Data for Growth and Well-Being, OECD Publishing, Paris. https://read.oecd-ilibrary.org/science-and-technology/data-driven-innovation_9789264229358-en#page11 , (23.07.2023).
• OECD (2019a). Artificial Intelligence in Society, www.oecd.org/going-digital/artificial- intelligence-in-society-eedfee77-en.htm, (23.05.2022).
• OECD (2019b). Recommendation of the Council on Artificial Intelligence, OECD/LEGAL/0449, https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 , (23.05.2022).
• OpenAI (2019). “Better Language Models and Their Implications”. 14.02.2019. https://openai.com/research/better-language-models .(13.08.2023).
• OPM (2015). “Statement by OPM Press Secretary on Background Investigations Incident”. https://www.opm.gov/news/releases/2015/09/cyber-statement-923 ,(03.07.2023)
• OPSI (2017). “Queensland Fire&Emergency Services Futures Service Demand Forecasting Model”. Observatory of Public Sector Innovation, Case Study Platform:Australia, https://oecd-opsi.org/innovations/queensland-fire-emergency-services-futures-service-demand-forecasting-model/ , (21.07.2023)
• OPSI (2018a). “R&D Platform for Investment and Evaluation (R&D PIE)”. Observatory of Public Sector Innovation, Case Study Platform: Korea OECD, https://oecd-opsi.org/innovations/rd-platform-for-investment-and-evaluation-rd-pie/ , (20.07.2023)
• OPSI (2018b). “Artificial Intelligence and the ‘Bomb-in- a -box’ Scenario: Risk-Based Oversight by Disruptive Technology”. Observatory of Public Sector Innovation, Case Study:Canada, https://oecd-opsi.org/innovations/artificial-intelligence-and-the-bomb-in-a-box-scenario-risk-based-oversight-by-disruptive-technology/ , (28.03.2023).
• OPSI (2018c). “Unlocking the potential of crowdsourcing for public decision-making with artifical intelligence”. Observatory of Public Sector Innovation, Case Study Platform: Belgium, https://oecd-opsi.org/innovations/unlocking-the-potential-of-crowdsourcing-for-public-decision-making-with-artificial-intelligence/ , (28.03.2023).
• Partnership for Public Service/IBM Center for the Business of Government (2019), More than Meets AI: Assessing the Impact of Artificial Intelligence on the Work of Government, Washington, DC, www.businessofgovernment.org/sites/default/files/More%20Than%20Meets%20AI.pdf. (25.05.2023)
• Platform:Canada, https://oecd-opsi.org/innovations/artificial-intelligence-and-the-bomb-in-a-box-scenario-risk-based-oversight-by-disruptive-technology/ , (23.07.2023)
• Raja, A. (2018). “How will GDPR affect AI?” Medium, 30 October, https://medium.com/datadriveninvestor/how-will-gdpr-affect-ai-3f10ed25e4c4. (25.05.2023)
• Renuka, D. K., Hamsapriya, T., Chakkaravarthi, M. R. ve Surya, P. L. (2011) "Spam Classification Based on Supervised Learning Using Machine Learning Techniques," 2011 International Conference on Process Automation, Control and Computing, Coimbatore, India, 1-7, doi: 10.1109/PACC.2011.5979035. https://ieeexplore.ieee.org/abstract/document/5979035 , (28.07.2023).
• Reshamwala A., Mishra D. ve Pawar P. (2013). “Review on natural language processing,” IRACST Engineering Science and Technology: An International Journal (ESTIJ), Vol. 3, n. 1, 113-116.
• Sandoiu, A. (2019). “Artificial Intelligence Better than humans at spotting lung cancer”. Medical News Today, 20.05.2019, https://www.medicalnewstoday.com/articles/325223 , (03.07.2023).
• Shafique, A. (2018). “Forget jobs. Will robots destroy our public services?”. 12.09.2018, RSA, https://www.thersa.org/blog/2018/09/forget-jobs.-will-robots-destroy-our-public-services, (25.05.2023).
• Soltani, A.A., Huang H., Wu J., Kulkarni T.D. ve Tenenbaum J.B. (2017).“Synthesizing 3D shapes via modeling multi-view depth maps and silhouettes with deep generative networks”, Computer Vision Foundation, http://openaccess.thecvf.com/content_cvpr_2017/papers/Soltani_Synthesizing_3D_Shapes_CVPR_2017_paper.pdf. (05.07.2023)
• Ubaldi, B., Fevre Le E.M., Petrucci E., Marchionni P., Biancalana C., Hiltunen N., Intravaia D.M. ve Yang C. (2019). “State of the art in the use of emerging Technologies in the sublic sector”, OECD Working Papers on Public Governance, No. 31, OECD Publishing, Paris, https://doi.org/10.1787/932780bc-en (12.07.2023)
• Viechnicki, P. ve Eggers W.D. (2017). How much time and money can AI save government? Cognitive technologies could free up hundreds of millions of public sector worker hours. Deloitte University Press, https://www2.deloitte.com/content/dam/insights/us/articles/3834_How-much-time-and-money-can-AI-save-government/DUP_How-much-time-and-money-can-AI-save-government.pdf (28.07.2023)
• Voulodimos, A., Doulamis, N., Doulamis, A., ve Protopapadakis, E. (2017). “Deep learning for computer vision: A brief review,”. Computational Intelligence and Neuroscience, Vol:18 <https://doi.org/10.1155/2018/7068349 > (03.03.2023)
• Whittaker, M., Crawford K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., West, S. M., Richardson, R., Schultz, J. ve Schwartz, O. (2018), AI Now Report, New York University, New York, https://ec.europa.eu/futurium/en/system/files/ged/ai_now_2018_report.pdf (26.06.2023)