Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration

Abstract

Artificial intelligence encompasses a wide range of approaches, methodologies, and techniques aimed at mimicking human intelligence in machines. In recent times, the concepts of Generative Artificial Intelligence (AI), Super AI, and Narrow AI have attracted considerable attention. Undoubtedly, the success of ChatGPT in capturing all attention has played a significant role in this. Artificial intelligence technology has a profound impact on all sectors, and sector representatives are striving to adapt to this technology more quickly. It is projected that artificial intelligence could generate an economic size of 13 trillion American dollars by 2030. Developments in artificial intelligence technologies undoubtedly lead to significant improvements in the functioning of public institutions and access for citizens. Artificial intelligence has the potential to be used in many public services, including security and defense, healthcare services, education, transportation and infrastructure, environmental and natural resource management, law and justice systems, among others. Therefore, evaluating the types of artificial intelligence, Narrow AI applications, and chatbots for public use is seen as highly beneficial from the perspective of public administration and the public sector. In our study, the topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration. Utilizing findings from both Turkish and English literature reviews, the importance and potential impacts of artificial intelligence within the public sector, along with current trends, have been comprehensively assessed. This research delves into the concepts of artificial intelligence and its subsets—super AI, generative AI, narrow AI, and chatbots—within the general framework of the public sector. China and the United States are pioneering and leading countries in terms of investment. Although the U.S. stands out in many areas regarding investment, China's integration of artificial intelligence with national strategies and its policies indicate that it may play a more dominant role in the future. There are four main implementation areas of artificial intelligence in the public sector: efficiency and automation, service delivery, data-driven governance, and ethical and regulatory challenges. A review of the literature reveals that the ethical, legal, and social implications of implementing artificial intelligence in the public sector require more careful consideration. The study makes a significant contribution to the field of artificial intelligence discussions in public administration and the public sector, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.

Keywords

• Artificial intelligence
• narrow ai
• generative ai
• chatbots
• public administration

Thanks

M.Damar was supported by the Scientific and Technological Research Council of Türkiye (TUBITAK) under the TUBITAK 2219 International Postdoctoral Research Fellowship program. He would like to thank the Upstream Lab, MAP, Li Ka Shing Knowledge Institute at the University of Toronto for its excellent hospitality.

References

1. Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185-199.
2. Agrawal A., Gans, J., & Goldfarb, A. (2019). Prediction machines: the simple economics of artificial intelligence. USA: Harvard Business Review Press.
3. Aithal, P. S. (2023). Super-Intelligent Machines-analysis of developmental challenges and predicted negative consequences. International Journal of Applied Engineering and Management Letters (IJAEML), 7(3), 109-141.
4. Akçakaya, M. (2017). E-Devlet Anlayışı Ve Türk Kamu Yönetiminde Edevlet Uygulamaları. Yüzüncü Yıl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (3), 8-31.
5. Akman, E., & Çetin, M. (2019). Yeni Kamu Yönetimi Anlayışının Bir Yansıması Olarak Dijital Dönüşüm Ofisi. IV. Uluslararası Stratejik ve Sosyal Araştırmalar Sempozyumu Kitabı, (pp.223-231), December, 19th, 2024, Burdur, Türkiye. https://www.isasor.org/ISASOR%20IV%20ABSTRACT%20BOOK.pdf
6. Akpınar, M. T. (2023). Akıllı Şehirler ve Yapay Zeka. TYB Akademi Dil Edebiyat & Sosyal Bilimler Dergisi, (37), 14-25.
7. Anderljung, M., Barnhart, J., Leung, J., Korinek, A., O'Keefe, C., Whittlestone, J., ... & Wolf, K. (2023). Frontier AI regulation: Managing emerging risks to public safety. arXiv preprint arXiv:2307.03718.
8. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, 36(2), 358-367.

9. Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government information quarterly, 37(4), 101490.
10. Avaner, T., & Çelik, M. (2021). Türkiye’de dijital dönüşüm ofisi ve yapay zeka yönetimi: Büyük Veri ve Yapay Zeka Daire Başkanlığı’nın geleceği üzerine. Medeniyet Araştırmaları Dergisi, 6(2), 1-18.
11. Aydın, Ö. (2023). Google Bard Generated Literature Review: Metaverse. Journal of AI, 7(1), 1-14. https://doi.org/10.61969/jai.1311271
12. Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations?. Academic Platform Journal of Engineering and Smart Systems, 11(3), 118-134.
13. Aydın, Ö., Karaarslan, E. (2022). OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare . In Ö. Aydın (Ed.), Emerging Computer Technologies 2 (pp. 22-31). İzmir Akademi Dernegi.Emerging Computer Technologies 2, Pp. 22-31
14. Babuta, A., Oswald, M., & Janjeva, A. (2020). Artificial intelligence and UK national security: policy considerations. Technical Report. RUSI, London.
15. Bilge, A. C. (2023). Bir yapay zekâ destekli dil modeli olan chatGPT’nin turizm sektöründe potansiyel ve hayata geçen uygulamaları. Journal of Recreation and Tourism Research, 10(3), 139-155.
16. Bozdoğanoğlu, B., Haspolat, İ., & Yücel, A. Kamu İdarelerinde Yapay Zekâ Kullanımının Ülke Uygulamaları ve Temel Kamusal İlkeler Çerçevesinde Değerlendirilmesi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), 1-32.
17. Bryant, A. (2023). AI Chatbots: Threat or Opportunity?. Informatics, 10(2), 49. https://doi.org/10.3390/informatics10020049
18. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, 4. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Notes%20from%20the%20frontier%20Modeling%20the%20impact%20of%20AI%20on%20the%20world%20economy/MGI-Notes-from-the-AI-frontier-Modeling-the-impact-of-AI-on-the-world-economy-September-2018.pdf
19. Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account. Public administration review, 81(5), 825-836.
20. Buxmann, P., & Schmidt, H. (2021). Grundlagen der Künstlichen Intelligenz und des Maschinellen Lernens. In: Buxmann, P., Schmidt, H. (eds) Künstliche Intelligenz. Springer Gabler, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61794-6_1
21. Caldwell, K. (2014). Are you next on the taxman’s hitlist. The Telegraph, 10. https://www.telegraph.co.uk/finance/personalfinance/tax/11092959/HMRC-targets-Are-you-next-on-the-taxmans-hitlist.html
22. Cuau, C. (2019). Applying artificial intelligence to citizen participation: the Youth4Climate case study. Citizenlab. Erişim Tarihi:25/05/2024. https://www.citizenlab.co/blog/civic-engagement/youth-for-climate-case-study/
23. Çarıkçı, O. (2010). Türkiyede e-devlet uygulamalari üzerine bir araştirma. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (12), 95-122.
24. Damar, M. (2022a). Dijital çağda bilişim sektörünün ihtiyacı olan yetkinlikler üzerine bir değerlendirme. Journal of Information Systems and Management Research, 4(1), 25-40.
25. Damar, M. (2022b). Dijital Dünyanın Dünü, Bugünü Ve Yarını: Bilişim Sektörünün Gelişimi Üzerine Değerlendirme. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 12(Dijitalleşme), 51-76.
26. Damar, M., & Aydın, Ö. (2021). Türkiye’nin 2010 Sonrası Yönetim Bilişim Sistemleri Alanında Uluslararası Q1 Dergilerinde Durumu. İzmir İktisat Dergisi, 36(4), 811-842.
27. Damar, M., & Özdağoğlu, G. (2021). Yazılım Sektörü ve Uluslararasılaşma, Politika Önerileri. Editörler, Ömer Aydın, Çağdaş Cengiz, Teknoloji ve Ululararası İlişkiler. İzmir: Nobel Yayın Evi.
28. Damar, M., Özdağoğlu, G., & Özveri, O. (2020). Üniversitelerde Dönüşüm Süreci Ve Araştırma Üniversitesi Yaklaşımı. Uluslararası Medeniyet Çalışmaları Dergisi, 5(2), 135-159.
29. De Sousa, W. G., de Melo, E. R. P., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392.
30. Dhara, S.K., Giri, A., Santra, A., Chakrabarty, D. (2023). Measuring the Behavioral Intention Toward the Implementation of Super Artificial Intelligence (Super-AI) in Healthcare Sector: An Empirical Analysis with Structural Equation Modeling (SEM). In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 754. Springer, Singapore. https://doi.org/10.1007/978-981-99-4932-8_42
31. Digital Transformation Office, (2023). Chatbot Uygulamları ve ChatGPT Örneği. Ankara: Türkiye Cumhuriyeti Cumhurbaşkanlığı Dijital Dönüşüm Ofisi.
32. Digital Transformation Office, (2024a). Türkiye Cumhuriyeti Cumhurbaşkanlığı Dijital Dönüşüm Ofisi. Ulusal Yapay Zekâ Stratejisi (UYZS) 2021-2025. Erişim Tarihi:25/05/2024. https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf.
33. Digital Transformation Office, (2024b). T.C. Dijital Dönüşüm Ofisi. T.C. Dijital Dönüşüm Ofisi. Erişim Tarihi:25/05/2024. https://cbddo.gov.tr/hakkimizda/
34. Efe, A. (2022). Yapay Zekâ Ortamındaki Dijital Kamu Yönetiminin Yol Haritası. Kamu Yönetimi ve Teknoloji Dergisi, 4(1), 99-130.
35. English Gov, (2017). China issues guideline on artificial intelligence development. The State Council The Peoples Republic of China. Erişim Tarihi: 25/05/2024. http://english.www.gov.cn/policies/latest_releases/2017/07/20/content_281475742458322.htm
36. Erbaş, M. S. (2023). Türk Kamu Yönetiminde Stratejik Yönetim ve Dijital Dönüşüm Bağlamında Yapay Zekanın Kullanımı. Türk İdare Dergisi,95(496),185-215.
37. Erdoğan, G. (2021). Yapay zekâ ve hukukuna genel bir bakış. Adalet Dergisi, (66), 117-192.
38. Fernández, A. (2019). Artificial intelligence in financial services. Banco de Espana Article, 3, 19.
39. Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobiezky, F., Zellinger, W., ... & Moser, B. (2020). Ai system engineering—key challenges and lessons learned. Machine Learning and Knowledge Extraction, 3(1), 56-83.
40. Frerichs, J.T.M. (2019). Empowering Our Recruiters: Leveraging Narrow Artificial Intelligence and Cloud-based Customer Relationship Management Tools to Enhance Systematic Recruiting. United States Marine Corps School of Advanced War.fighting Marine C01ps University. Quantico, Virginia, USA.
41. Gezici, H. S. (2023). Kamu yönetiminde yapay zeka: Avrupa Birliği. Uluslararası Akademik Birikim Dergisi, 6(2), 111-128.
42. Goosen, R., Rontojannis, A., Deutscher, S., Rogg, J., Bohmayr, W., & Mkrtchian, D. (2018). Artificial Intelligence Is A Threat To Cybersecurity. It’s Also A Solution. Boston Consulting Group (BCG), Tech. Rep. https://boston-consulting-group-brightspot.s3.amazonaws.com/img-src/BCG-Artificial-Intelligence-Is-a-Threat-to-Cyber-Security-Its-Also-a-Solution-Nov-2018_tcm9-207468.pdf
43. Goyal, P., Pandey, S., & Jain, K. (2018). Deep learning for natural language processing. New York: Apress.
44. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). Viewpoint: when will aI exceed human performance. EvidencefromaIexperts. Journal of Artificial Inteligence Research,62(2018), 729-754.
45. Gtech, (2021). Yapay Zeka Nedir, Yapay Zeka Hakkında Bilmeniz Gerekenler. Erişim Tarihi: 25/05/2024. https://www.gtech.com.tr/yapay-zeka-nedir-yapay-zeka-hakkinda-bilmeniz-gerekenler/
46. Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
47. Huang, C., & Gan, K. (2023). Enhancing Citizen Engagement in Smart Cities with Chatbot. International Journal of Smart Systems, 1(1), 34-39.
48. Ingrams, A., Kaufmann, W., & Jacobs, D. (2022). In AI we trust? Citizen perceptions of AI in government decision making. Policy & Internet, 14(2), 390-409.
49. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.
50. Koçyiğit, A., & Darı, A. B. (2023). Yapay zekâ iletişiminde chatgpt: insanlaşan dijitalleşmenin geleceği. Stratejik Ve Sosyal Araştırmalar Dergisi, 7(2), 427-438.
51. Kopka, B. (2011). Theoretical aspects of using virtual advisors in public administration . 3rd International Conference – New Economic Challenges. Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic
52. Kouziokas, G. N. (2016). Artificial intelligence and crime prediction in public management of transportation safety in urban environment. In Proceedings of the 3rd conference on sustainable urban mobility (pp. 534-539). Volos: University of Thessaly.
53. Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and brain sciences, 40, e253.
54. Lambert, J., & Stevens, M. (2023). ChatGPT and generative AI technology: A mixed bag of concerns and new opportunities. Computers in the Schools, (Online),1-25. https://doi.org/10.1080/07380569.2023.2256710
55. Livingston, C. (2023). ChatGPT, The rise of generative AI. Erişim Tarihi: 2/05/2024. https://www.cio.com/article/474809/chatgpt-the-rise-of-generative-ai.html
56. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. April 25 - 30, 2020, Honolulu, USA.
57. Lu, Y., Qian, D., Fu, H., & Chen, W. (2018). Will supercomputers be super-data and super-AI machines?. Communications of the ACM, 61(11), 82-87.
58. Luck, M. (2024). Freedom, AI and God: why being dominated by a friendly super-AI might not be so bad. AI & Society, Online(2024),1-8.
59. Lundy, J., Keast, R., Farr-Wharton, B., Omari, M., Teo, S., & Bentley, T. (2021). Utilising a capability maturity model to leverage inclusion and diversity in public sector organisations. Australian Journal of Public Administration, 80(4), 1032-1045.
60. Maciejewski, M. (2017). To do more, better, faster and more cheaply: Using big data in public administration. International Review of Administrative Sciences, 83(1_suppl), 120-135. https://doi.org/10.1177/0020852316640058
61. Maragno, G., Tangi, L., Gastaldi, L., & Benedetti, M. (2023). AI as an organizational agent to nurture: effectively introducing chatbots in public entities. Public Management Review, 25(11), 2135-2165.
62. Maslej, N., Fattorini, L., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., ... & Perrault, R. (2023). Artificial intelligence index report 2023. arXiv preprint arXiv:2310.03715.
63. Mehr, H., Ash, H., & Fellow, D. (2017). Artificial intelligence for citizen services and government. Ash Center for Democratic Governance and Innovation. Harvard Kennedy School, no. August, 1-12. https://creatingfutureus.org/wp-content/uploads/2021/10/Mehr-2017-AIforGovCitizenServices.pdf
64. Miller, S. (2024). Unveiling the Synergy: Exploring Advances in Applied Artificial Intelligence and Narrow AI (No. 11651). EasyChair. https://easychair.org/publications/preprint_download/d1PB
65. Ministry of Foreign Affairs. (2024). Hızır yapay zekâ uygulaması. Hızır yapay zekâ uygulaması. Erişim Tarihi:25/05/2024. http://www.konsolosluk.gov.tr/UseFulLinks/Index
66. Ministry of National Education. (2024). T.C. Milli Eğitim Bakanlığı. Eba ve MEB asistan. Erişim Tarihi:25/05/2024. https://www.meb.gov.tr/eba-asistan-uzaktan-egitimde-cevapsiz-soru-birakmayacak/haber/20829/tr
67. Ministry of Treasury and Finance. (2024). T.C. Hazine ve Maliye Bakanlığı. GİBİ uygulaması. Erişim Tarihi: 25/05/2024. https://www.gib.gov.tr/mobil-uygulamalar-0
68. Misuraca, G., & Van Noordt, C. (2020). AI Watch-Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU. JRC Research Reports, (JRC120399).
69. Moser, B. (2022). Modeling & Engineering Beyond Narrow AI. Erişim Tarihi: 25/05/2024. https://www.software-center.se/wp-content/uploads/2022/05/2022-05-09-SC_BeyondNarrowAI.pdf
70. Önder, M., & Saygılı, H. (2018). Yapay Zekâ Ve Kamu Yönetimine Yansimalari. Türk İdare Dergisi, 2(487), 629-670.
71. Özdemir, G. S. (2022). Yapay Zekada Küresel Gelişmeler ve Trendler: Türkiye’nin Yeri Nedir?. Erişim Tarih: 25/05/2024. https://kriterdergi.com/dosya-teknoloji/yapay-zekada-kuresel-gelismeler-ve-trendler-turkiyenin-yeri-nedir
72. Page, J., Bain, M., & Mukhlish, F. (2018). The risks of low level narrow artificial intelligence. In 2018 IEEE international conference on intelligence and safety for robotics (ISR) (pp. 1-6). IEEE, 24-27 Aug. 2018, Shenyang, China.
73. Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409-413
74. Rawat, R., Goyal, H. R., & Sharma, S. (2023). Artificial Narrow Intelligence Techniques in Intelligent Digital Financial Inclusion System for Digital Society. In 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-5). IEEE, Mathura, India, March 3-4, 2023.
75. Revell, T. (2017). Go-playing super AI transcends humanity. New scientist, (3148), 1-9.
76. Sanghrajka J. (2024). Taxation UK, HMRC’s Connect computer and investigations. Erişim Tarihi: 25/05/2024. https://www.taxation.co.uk/articles/hmrc-s-connect-computer-and-investigations
77. Sarıtürk, M. (2022). Dijital Dönüşüm Döneminde Kamu Yönetimi ve Dijital Hükümet. Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (42), 555-603.
78. Saveliev, A., & Zhurenkov, D. (2021). Artificial intelligence and social responsibility: the case of the artificial intelligence strategies in the United States, Russia, and China. Kybernetes, 50(3), 656-675.
79. Serçemeli, M. (2018). Muhasebe Ve Denetim Mesleklerinin Dijital Dönüşümünde Yapay Zekâ. Electronic Turkish Studies, 13(30),369-386.
80. Sharon, T., & Gellert, R. (2023). Regulating Big Tech expansionism? Sphere transgressions and the limits of Europe’s digital regulatory strategy. Information, Communication & Society, (Online),1-18. https://doi.org/10.1080/1369118X.2023.2246526
81. Shawar, B. A., & Atwell, E. (2007). Chatbots: are they really useful?. Journal for Language Technology and Computational Linguistics, 22(1), 29-49.
82. Suebvises, P. (2018). Social capital, citizen participation in public administration, and public sector performance in Thailand. World Development, 109, 236-248.
83. Şahnagil, S. (2023). Kamu Yönetimi ve Yapay Zekâ İlişkisi. Editör Ö. Dündar, İktisadi ve İdari Bilimler Alanında Teori, Uygulama ve Güncel Tartışmalar (ss.23-40). Ankara: Gazi Kitabevi.
84. Şentürk, Ö. (2023). İç Denetim Faaliyetlerinde Yapay Zekadan Beklentiler: Chatgpt Uygulaması Örneği. TIDE AcademIA Research, 4(2), 51-82.
85. Tamer, H. Y., & Övgün, B. (2020). Yapay zeka bağlamında dijital dönüşüm ofisi. Ankara Üniversitesi SBF Dergisi, 75(2), 775-803.
86. Tanrıverdi, A. (2021). Yapay zekânın kamu hizmetinin sunumuna etkileri. Adalet Dergisi, (66), 293-314.
87. TBD, (2020). Türkiye’de Yapay Zekanın Gelişimi İçin Görüş ve Öneriler. Türkiye Bilişim Derneği Kavramsal Rapor. Erişim Tarihi: 25/05/2024. https://www.tbd.org.tr/pdf/yapay-zeka-raporu.pdf
88. Turchin, A. (2018). Narrow AI Nanny: Reaching Strategic Advantage via Narrow AI to Prevent Creation of the Dangerous Superintelligence. Erişim Tarihi: 25/05/2024. https://philpapers.org/rec/TURNAN-3
89. Ulaşan, F. (2023). Koronavirüsle Mücadelede Yapay Zekânin Yerinin Kamu Yönetimi Temelinde Değerlendirilmesi. In International Mediterranean Congress.(Ed. B. Arslan and M. Erdoğan). Mersin: Iksad Global.
90. Uslu, H. (2023). Dijital Dönüşüm ve Kamu Hizmetleri Yönetimde Yenilikçi Yaklaşımlar ve Zorluklar. Uluslararası Politik Araştırmalar Dergisi, 9(3), 15-31.
91. Uzun, M. M., Yıldız, M., & Önder, M. (2022). Big Questions of AI in Public Administration and Policy. Siyasal: Journal of Political Sciences, 31(2), 423-442.
92. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596-615.
93. Wogu, I. A. P., Misra, S., Assibong, P. A., Ogiri, S. O., Damasevicius, R., & Maskeliunas, R. (2018). Super-Intelligent Machine Operations in Twenty-First-Century Manufacturing Industries: A Boost or Doom to Political and Human Development?. Towards Extensible and Adaptable Methods in Computing, 209-224.
94. Yalçın, A. (2024). Türkiye'de Kamu Kurumlarının Toplum İçin Geliştirdiği Yapay Zekâ Uygulamaları. İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi, 16(2), 185-215.
95. Young, M. M., Bullock, J. B., & Lecy, J. D. (2019). Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance, 2(4), 301-313.