Humanocentrična bezbednost u Društvu 5.0: Razmatranje algoritamske pristrasnosti i metodoloških ograničenja u prediktivnoj policiji

Autori

DOI:

https://doi.org/10.51738/kpolisa.2025.3r.006

Ključne reči:

Društvo 5.0, bezbednost, prediktivna policija, veštačka inteligencija

Apstrakt

Društvo 5.0, zamišljeno kao napredna, humanocentrična civilizacija u kojoj se fizička i digitalna sfera međusobno neprimetno dopunjuju, predstavlja održiv model funkcionisanja ljudskih zajednica na sve prenaseljenijoj planeti. U takvom okruženju očekuje se sve dublja simbioza društvenih procesa i tehnoloških inovacija, pri čemu se sektor bezbednosti izdvaja kao jedno od ključnih područja ove integracije. U domenu javne bezbednosti posebno se ističe prediktivna policija: njeni modeli, zasnovani na veštačkoj inteligenciji i analitici velikih podataka, omogućavaju anticipaciju kriminalnih obrazaca i potencijalno efikasniju prevenciju rizika. Ovaj rad razmatra prediktivnu policiju kao transformativni pristup sprovođenju zakona, istovremeno problematizujući njene duboko ukorenjene izazove- naročito u kontekstu vrednosnog okvira Društva 5.0, koje teži tome da tehnološki napredak ostane podređen ljudskom dostojanstvu, pravednosti i društvenoj inkluziji. Kroz analizu studija slučaja iz Čikaga, Londona i Tokija, rad identifikuje operativne prednosti prediktivnih tehnika, ali ističe i ključne probleme kao što su algoritamska pristrasnost, nedostatak transparentnosti, nezadovoljavajući kvalitet podataka i metodološka ograničenja koja mogu ugroziti pravičnost i legitimitet policijskih intervencija. Nalazi pokazuju da, iako prediktivni algoritmi mogu doprineti unapređenju preventivnih strategija, njihova primena mora biti uokvirena jasno definisanom normativnom strukturom koja uključuje tehničku robusnost, nezavisni nadzor, institucionalnu odgovornost i aktivno učešće građana. U skladu sa principima Društva 5.0, rad zaključuje da uspešna primena prediktivne policije zahteva razvoj sistema koji su etički utemeljeni, metodološki transparentni i usmereni ka zaštiti ljudskih prava - obezbeđujući da tehnologija služi društvu, a ne obrnuto.

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Reference

Advisory concerning the Chicago Police Department’s predictive Risk models - Chicago O fice of Inspector General. (2023, August 8). Chicago Office of Inspector General. https://igchicago.org/publications/advisory-concerning-the-chicago-police-departments-predictive risk-models/

Belcic, I. (n.d). What is classification in machine learning? IBM. https://www.ibm.com/think/topics/classification-machine-learning

BenGal, I. (2007). Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability. https://doi.org/10.1002/9780470061572.eqr089

Biggs, M. (2013). Prophecy, Self-Fulfi ling/Self-Defeating. Encyclopedia of Philosophy and the Social Sciences. https://doi.org/10.4135/9781452276052.n292

Bjelajac, Ž., Filipović, A. (2021). Artificial Inte ligence: Human Ethics in Non-Human Entities. In Proceedings of the 3rd Virtual International Conference „Path to a Knowledge Society-Managing Risks and Innovation - PaKSoM 2021”.

Bjelajac, Ž., & Bajac, M. (2022). Blockchain Technology and Money Laundering. Pravo - Teorija i Praksa, 39(2), 21–38. https://doi.org/10.5937/ptp2202021B

Bjelajac, Željko, Filipović, A. M., & Stošić, L. V. (2022). Internet Addiction Disorder (IAD) as a Consequence of the Expansion of Information Technologies. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 10(3), 155–165. https://doi.org/10.23947/2334-8496-2022-10-3-155-165

Brayne, S. (2020). Predict and surveil. In Oxford University Press eBooks. https://doi.org/10.1093/oso/9780190684099.001.0001

Coşan, B. (2021). Toplum 5.0’ın Mimarı Japonya’da Dezavantajlı Gruplar: Freeter, Hikikomori ve Parasaito Shinguru [Disadvantaged groups in Japan, the architect of Society 5.0: Freeter, Hikikomori, and Parasaito Shinguru]. Sosyal Siyaset Konferansları Dergisi / Journal of Social Policy Conferences, 81, 393–419.

DaViera, A. L., Uriostegui, M., Gottlieb, A., & Onyeka, O. (2023). Risk, race, and predictive policing: A critical race theory analysis of the strategic subject list. American Journal of Community Psychology, 73(1–2), 91–103. https://doi.org/10.1002/ajcp.12671

Degeling, M., & Berendt, B. (2017). What is wrong about Robocops as consultants? A technology-centric critique of predictive policing. AI & Society, 33(3), 347–356. https://doi.org/10.1007/s00146-0170730-7

Egbert, S., & Esposito, E. (2024). Algorithmic crime prevention. From abstract police to precision policing. Policing & Society, 34(6), 521–534. https://doi.org/10.1080/10439463.2024.2326516

Ema, A. (2020). AI and Society: A Pathway from Interdisciplinary-alone to Interdisciplinary Research. Trends In the Sciences, 25(7), 7_29-7_37. https://doi.org/10.5363/tits.25.7_29

Eterno, J. A., & Silverman, E. B. (2006). The New York City Police Department’s Compstat: Dream or nightmare? International Journal of Police Science & Management, 8(3), 218–231. https://doi.org/10.1350/ijps.2006.8.3.218

Feathers, T., & Feathers, T. (2024, July 27). Police are te ling ShotSpotter to alter evidence from Gunshot Detecting AI. VICE. https://www.vice.com/en/article/police-are-te ling-shotspotter-to-alter-evidence-from-gunshot detecting-ai/Huang, S., Wang, B., Li, X., Zheng, P., Mourtzis, D., & Wang, L. (2022). Industry 5.0 and Society 5.0 Comparison, complementation and co-evolution. Journal of Manufacturing Systems, 64, 424-428. https://doi.org/10.1016/j.jmsy.2022.07.010

Hume, D. (1739). A treatise of human nature. In Oxford University Press eBooks (pp. 1–689). https://doi.org/10.1093/oseo/instance.00032872

Itakura, D. (n.d). NPA to use AI to identify leaders of ‘tokuryu’ crime groups. The Asahi Shimbun. https://www.asahi.com/ajw/articles/15995228

Jeffrey, R. (1992). Probability and the Art of Judgment, Cambridge University Press. Jonker, A. & Rogers, J. (n.d). What is algorithmic bias? IBM. https://www.ibm.com/think/topics/algorithmic-bias

Juba, B., & Le, H. S. (2019). Precision-Reca l versus Accuracy and the Role of Large Data Sets. Proceedings of the AAAI Conference on Artificial Inte ligence, 33(01), 4039–4048. https://doi.org/10.1609/aaai.v33i01.33014039

Kenge, R. (2020). Machine learning, its limitations, and solutions over IT. International Journal of Applied Research on Information Technology and Computing, 11(2), 73. https://doi.org/10.5958/0975-8089.2020.00009.3

London Assembly. (2013). Predictive policing. London Assembly. https://www.london.gov.uk/who-we-are/what-london-assembly-does/questions-mayor/find-an-answer/predictive-policing

Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 13(5), 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x

Meijer, A., & Wessels, M. (2019). Predictive Policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031–1039. https://doi.org/10.1080/01900692.2019.1575664

Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi, A. L., & Brantingham, P. J. (2015). Randomized contro led field trials of predictive policing. Journal of the American Statistical Association, 110(512), 1399–1411. https://doi.org/10.1080/01621459.2015.1077710

Perry, W. L., McInnis, B., Price, C. C., Smith, S., & Ho lywood, J. S. (2013, September 25). Predictive Policing: The role of crime forecasting in law enforcement operations. RAND. https://www.rand.org/pubs/research_reports/RR233.html

Rawashdeh, S. (n.d) AI's mysterious ‘black box’ problem, explained. University of Michigan-Dearborn, https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained

Sheng, Y., Zhang, G., Zhang, Y., Luo, M., Pang, Y., & Wang, Q. (2023). A multimodal data sensing and feature learning-based self-adaptive hybrid approach for machining quality prediction. Advanced Engineering Informatics, 59, 102324. https://doi.org/10.1016/j.aei.2023.102324

Tsunoda, T. & Komatsu, S. (2022). The landscape of AI ethics and law in Japan, PAI Summit 2022, Global Partnership on Artificial Inte ligence, https://www.nishimura.com/en/knowledge/seminars/20221121-93381

Tucek, A. (n.d). Constraining Big Brother: The legal deficiencies surrounding Chicago’s use of the strategic subject list. The University of Chicago Legal Forum. https://legal-forum.uchicago.edu/print-archive/constraining-big-brother-legal-deficiencies-surrounding-chicagos-use-strategic

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2025-12-22

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