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Yapay Zeka Destekli Kolluk Kuvvetleri Uygulamalarında Akademik Eğilimlerin Analizi

Yıl 2026, Cilt: 8 Sayı: 1 , 59 - 87 , 24.03.2026
https://doi.org/10.58307/kaytek.1699454
https://izlik.org/JA39KH38FX

Öz

Yapay zeka, kolluk kuvvetlerinin suç analizi, gözetim ve adli bilişim gibi operasyonlarını daha etkili hale getirmek için giderek daha fazla kullanılmaktadır. Bu çalışma, yapay zekanın kolluk kuvvetleriyle kesişimini bibliyometrik ve semantik analiz yöntemleriyle inceleyerek akademik literatürdeki eğilimleri ortaya koymaktadır. Web of Science (WoS) veri tabanında yapılan tarama sonucunda 238 akademik çalışma analiz edilmiştir. Bibliyometrik analiz kapsamında yayınların sayısal dağılımı, atıf ağları, anahtar kelime frekansları ve bilimsel işbirlikleri değerlendirilmiştir. Semantik analiz ile çalışmalardaki temel temalar veri madenciliği teknikleri ile belirlenmiştir. Bu şekilde beş ana küme tespit edilmiştir: dijital medya ve yasadışı içerik takibi, yüz tanıma teknolojisi ve hukuki uygulamaları, suç tahmini ve veri tabanlı hukuki modeller, dijital adli bilişim ve kanıt analizi, yapay zeka ve hukuki düzenlemeler. Özellikle yapay zeka tabanlı suç tahmini sistemleri, adli bilişim uygulamaları ve yüz tanıma teknolojilerinin kolluk kuvvetlerinde kullanımına dair araştırmaların yoğunlaştığı tespit edilmiştir. Bu çalışma, yapay zekanın güvenlik politikalarında önemli fırsatlar sunduğunu ancak etik, hukuki ve mahremiyet konularında dikkatli düzenlemeler gerektirdiğini vurgulamaktadır. Şeffaflık, hesap verebilirlik ve algoritmik önyargıların önlenmesi için daha fazla araştırmaya ihtiyaç duyulmaktadır.

Kaynakça

  • Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Azzutti, A. (2022). AI trading and the limits of EU law enforcement in deterring market manipulation. Computer Law & Security Review, 45, 105690. https://doi.org/10.1016/j.clsr.2022.105690
  • Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024). The Role of AI in Cyber Security: Safeguarding Digital Identity. Journal of Information Security, 15(02), 245-278. https://doi.org/10.4236/jis.2024.152015
  • Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., Koh, T. W., & Sherazi, H. H. R. (2021). Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities. IEEE Access, 9, 47516-47529. https://doi.org/10.1109/ACCESS.2021.3068306
  • Castelli, M., Sormani, R., Trujillo, L., & Popovič, A. (2017). Predicting per capita violent crimes in urban areas: an artificial intelligence approach. Journal of Ambient Intelligence and Humanized Computing, 8(1), 29-36. https://doi.org/10.1007/s12652-015-0334-3
  • Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109, 1-14. https://doi.org/10.1016/j.jbusres.2019.10.039
  • Elkin-Koren, N. (2020). Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720932296
  • Fidalgo, E., Alegre, E., Fernández-Robles, L., & González-Castro, V. (2019). Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering. Digital Investigation, 30, 12-22. https://doi.org/10.1016/j.diin.2019.05.004
  • Irons, A., & Lallie, H. (2014). Digital Forensics to Intelligent Forensics. Future Internet, 6(3), 584-596. https://doi.org/10.3390/fi6030584
  • Jarrett, A., & Choo, K. R. (2021). The impact of automation and artificial intelligence on digital forensics. WIREs Forensic Science, 3(6). https://doi.org/10.1002/wfs2.1418
  • Keyvanpour, M. R., Javideh, M., & Ebrahimi, M. R. (2011). Detecting and investigating crime by means of data mining: a general crime matching framework. Procedia Computer Science, 3, 872-880. https://doi.org/10.1016/j.procs.2010.12.143
  • Kişisel Verileri Koruma Kurulu (2025), Deepfake Bilgi Notu, KVKK Yayınları.
  • Lucas, K. T. (2022). Deepfakes and Domestic Violence: Perpetrating Intimate Partner Abuse Using Video Technology. Victims & Offenders, 17(5), 647-659. https://doi.org/10.1080/15564886.2022.2036656
  • Medapati, P. K., Tejo Murthy, P. H. S., & Sridhar, K. P. (2020). LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities. Transactions on Emerging Telecommunications Technologies, 31(12). https://doi.org/10.1002/ett.3843
  • Mobilio, G. (2023). Your face is not new to me – Regulating the surveillance power of facial recognition technologies. Internet Policy Review, 12(1). https://doi.org/10.14763/2023.1.1699
  • Passas, I. (2024). Bibliometric Analysis: The Main Steps. Encyclopedia, 4(2), 1014-1025. https://doi.org/10.3390/encyclopedia4020065
  • Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. WIREs Data Mining and Knowledge Discovery, 7(4). https://doi.org/10.1002/widm.1208
  • Rademacher, T. (2020). Artificial Intelligence and Law Enforcement. Içinde Regulating Artificial Intelligence (ss. 225-254). Springer International Publishing. https://doi.org/10.1007/978-3-030-32361-5_10
  • Raposo, V. L. (2023). The Use of Facial Recognition Technology by Law Enforcement in Europe: a Non-Orwellian Draft Proposal. European Journal on Criminal Policy and Research, 29(4), 515-533. https://doi.org/10.1007/s10610-022-09512-y
  • Redmond, M., & Baveja, A. (2002). A data-driven software tool for enabling cooperative information sharing among police departments. European Journal of Operational Research, 141(3), 660-678. https://doi.org/10.1016/S0377-2217(01)00264-8
  • Sanchez, L., Grajeda, C., Baggili, I., & Hall, C. (2019). A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM). Digital Investigation, 29, S124-S142. https://doi.org/10.1016/j.diin.2019.04.005
  • Smith, M., & Miller, S. (2022). The ethical application of biometric facial recognition technology. AI & SOCIETY, 37(1), 167-175. https://doi.org/10.1007/s00146-021-01199-9
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039
  • Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., & Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing, 29(4), 51-71. https://doi.org/10.4018/JOEUC.2017100103
  • Verma, S., & Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253-261. https://doi.org/10.1016/j.jbusres.2020.06.057
  • Zuiderveen Borgesius, F. J. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. The International Journal of Human Rights, 24(10), 1572-1593. https://doi.org/10.1080/13642987.2020.1743976

An Analysis of Academic Trends in Artificial Intelligence-Supported Law Enforcement Applications

Yıl 2026, Cilt: 8 Sayı: 1 , 59 - 87 , 24.03.2026
https://doi.org/10.58307/kaytek.1699454
https://izlik.org/JA39KH38FX

Öz

Artificial intelligence is increasingly being utilized by law enforcement agencies to enhance the effectiveness of operations in crime analysis, surveillance, and digital forensics. This study examines the intersection of artificial intelligence and law enforcement through bibliometric and semantic analysis, revealing trends in the academic literature. A total of 238 academic studies were analyzed based on a search conducted in the Web of Science (WoS) database. The bibliometric analysis evaluated the numerical distribution of publications, citation networks, keyword frequencies, and scientific collaborations. In the semantic analysis, key themes within the studies were identified using data mining techniques. As a result, five main clusters were determined: digital media and illicit content monitoring, facial recognition technology and legal applications, crime prediction and data-driven legal models, digital forensics and evidence analysis, and artificial intelligence and legal regulations. The findings indicate a growing research focus on artificial intelligence based crime prediction systems, forensic applications, and facial recognition technologies in law enforcement. This study highlights the opportunities artificial intelligence presents for security policies while emphasizing the need for careful regulations regarding ethical, legal, and privacy concerns. Greater transparency, accountability, and efforts to mitigate algorithmic bias are necessary for the responsible integration of artificial intelligence in law enforcement.

Kaynakça

  • Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Azzutti, A. (2022). AI trading and the limits of EU law enforcement in deterring market manipulation. Computer Law & Security Review, 45, 105690. https://doi.org/10.1016/j.clsr.2022.105690
  • Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024). The Role of AI in Cyber Security: Safeguarding Digital Identity. Journal of Information Security, 15(02), 245-278. https://doi.org/10.4236/jis.2024.152015
  • Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., Koh, T. W., & Sherazi, H. H. R. (2021). Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities. IEEE Access, 9, 47516-47529. https://doi.org/10.1109/ACCESS.2021.3068306
  • Castelli, M., Sormani, R., Trujillo, L., & Popovič, A. (2017). Predicting per capita violent crimes in urban areas: an artificial intelligence approach. Journal of Ambient Intelligence and Humanized Computing, 8(1), 29-36. https://doi.org/10.1007/s12652-015-0334-3
  • Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109, 1-14. https://doi.org/10.1016/j.jbusres.2019.10.039
  • Elkin-Koren, N. (2020). Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720932296
  • Fidalgo, E., Alegre, E., Fernández-Robles, L., & González-Castro, V. (2019). Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering. Digital Investigation, 30, 12-22. https://doi.org/10.1016/j.diin.2019.05.004
  • Irons, A., & Lallie, H. (2014). Digital Forensics to Intelligent Forensics. Future Internet, 6(3), 584-596. https://doi.org/10.3390/fi6030584
  • Jarrett, A., & Choo, K. R. (2021). The impact of automation and artificial intelligence on digital forensics. WIREs Forensic Science, 3(6). https://doi.org/10.1002/wfs2.1418
  • Keyvanpour, M. R., Javideh, M., & Ebrahimi, M. R. (2011). Detecting and investigating crime by means of data mining: a general crime matching framework. Procedia Computer Science, 3, 872-880. https://doi.org/10.1016/j.procs.2010.12.143
  • Kişisel Verileri Koruma Kurulu (2025), Deepfake Bilgi Notu, KVKK Yayınları.
  • Lucas, K. T. (2022). Deepfakes and Domestic Violence: Perpetrating Intimate Partner Abuse Using Video Technology. Victims & Offenders, 17(5), 647-659. https://doi.org/10.1080/15564886.2022.2036656
  • Medapati, P. K., Tejo Murthy, P. H. S., & Sridhar, K. P. (2020). LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities. Transactions on Emerging Telecommunications Technologies, 31(12). https://doi.org/10.1002/ett.3843
  • Mobilio, G. (2023). Your face is not new to me – Regulating the surveillance power of facial recognition technologies. Internet Policy Review, 12(1). https://doi.org/10.14763/2023.1.1699
  • Passas, I. (2024). Bibliometric Analysis: The Main Steps. Encyclopedia, 4(2), 1014-1025. https://doi.org/10.3390/encyclopedia4020065
  • Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. WIREs Data Mining and Knowledge Discovery, 7(4). https://doi.org/10.1002/widm.1208
  • Rademacher, T. (2020). Artificial Intelligence and Law Enforcement. Içinde Regulating Artificial Intelligence (ss. 225-254). Springer International Publishing. https://doi.org/10.1007/978-3-030-32361-5_10
  • Raposo, V. L. (2023). The Use of Facial Recognition Technology by Law Enforcement in Europe: a Non-Orwellian Draft Proposal. European Journal on Criminal Policy and Research, 29(4), 515-533. https://doi.org/10.1007/s10610-022-09512-y
  • Redmond, M., & Baveja, A. (2002). A data-driven software tool for enabling cooperative information sharing among police departments. European Journal of Operational Research, 141(3), 660-678. https://doi.org/10.1016/S0377-2217(01)00264-8
  • Sanchez, L., Grajeda, C., Baggili, I., & Hall, C. (2019). A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM). Digital Investigation, 29, S124-S142. https://doi.org/10.1016/j.diin.2019.04.005
  • Smith, M., & Miller, S. (2022). The ethical application of biometric facial recognition technology. AI & SOCIETY, 37(1), 167-175. https://doi.org/10.1007/s00146-021-01199-9
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039
  • Subramaniyaswamy, V., Logesh, R., Abejith, M., Umasankar, S., & Umamakeswari, A. (2017). Sentiment Analysis of Tweets for Estimating Criticality and Security of Events. Journal of Organizational and End User Computing, 29(4), 51-71. https://doi.org/10.4018/JOEUC.2017100103
  • Verma, S., & Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253-261. https://doi.org/10.1016/j.jbusres.2020.06.057
  • Zuiderveen Borgesius, F. J. (2020). Strengthening legal protection against discrimination by algorithms and artificial intelligence. The International Journal of Human Rights, 24(10), 1572-1593. https://doi.org/10.1080/13642987.2020.1743976
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Güvenliği Yönetimi, Bilgisayar Sistemlerinin Adalet, Hesap Verebilirlik, Şeffaflık, Güven ve Etiği, Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Bora Aslan 0000-0002-8069-8204

Füsun Yavuzer Aslan 0000-0001-7096-3425

Gönderilme Tarihi 14 Mayıs 2025
Kabul Tarihi 28 Aralık 2025
Yayımlanma Tarihi 24 Mart 2026
DOI https://doi.org/10.58307/kaytek.1699454
IZ https://izlik.org/JA39KH38FX
Yayımlandığı Sayı Yıl 2026 Cilt: 8 Sayı: 1

Kaynak Göster

APA Aslan, B., & Yavuzer Aslan, F. (2026). Yapay Zeka Destekli Kolluk Kuvvetleri Uygulamalarında Akademik Eğilimlerin Analizi. Kamu Yönetimi ve Teknoloji Dergisi, 8(1), 59-87. https://doi.org/10.58307/kaytek.1699454