Absztrakt
Aim: The study is on the emerging role of artificial intelligence in the forensic sciences. After clarifying the basic concepts and a brief historical overview, the possibilities of using AI in various forensic fields are discussed: genetics, pattern recognition, chemistry, toxicology, anthropology, forensic medicine, and scene reconstruction.
Methodology: The study synthesises several recently published international papers.
Findings: The penetration of the application of artificial intelligence into some fields of science is undoubtedly an ongoing process. Most of the varied forensic fields also cannot avoid this development. Analysing large databases unmanageable with traditional methods, pattern recognition, and machine learning can all be important tools for forensic science. However, an important conclusion is that AI is a supporter of human expert work, not a substitute.
Value: In the field of forensic sciences, no such detailed summary article has been published in Hungarian so far.
Hivatkozások
Akhgar, B., Bayerl, P., Mounier, G., Linden, R. & Waites, B. (2022). AP4AI. European Law Enforcement Research Bulletin, (6), 47–56.
Apasrawirote, D., Boonchai, P., Muneesawang, P., Nakhonkam, W. & Bunchu, N. (2022). Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles. Scientific Reports, (12), 4753. https://doi.org/10.1038/s41598-022-08823-8
Almazrouei, M. A., Dror, I. E. & Morgan, R. M. (2019). The forensic disclosure model: what should be disclosed to, and by, forensic experts? Int J Law Crime Justice, (59), 100330. https://doi.org/10.1016/j.ijlcj.2019.05.003
Barros, A. I., van der Zwet, K., Westerveld, J. & Schreurs, W. (2022). AI Potential to Uncover Criminal Modus Operandi Features. European Law Enforcement Research Bulletin, (6), 255–263.
Bobbili, R., Ramakrishna, B. & Madhu, V. (2020). An artificial intelligence model for ballistic performance of thin plates. Mechanics Based Design of Structure and Machines, 51(1), 327–338. http://dx.doi.org/10.1080/15397734.2020.1843487
Clarke, R. V. (2004). Technology, criminology and crime science. European Journal on Criminal Policy and Research, 10(1), 55–63. http://dx.doi.org/10.1023/B:CRIM.0000037557.42894.f7
Chin, J. M., Ribeiro, G. & Rairden, A. (2019). Open forensic science. Journal of Law and the Biosciences, 6(1), 255–288. https://doi.org/10.1093/jlb/lsz009
Cole, S. A. (2016). Scandal, fraud, and the reform of forensic science: the case of fingerprint analysis. West Virginia University, 119(2), 524–548.
Czebe A. (2021) A mesterséges intelligencia alkalmazásának elméleti keretei a büntetőeljárásban [The theoretical framework of the application of artificial intelligence in criminal procedure]. Kúriai Döntések, 16(7), 1111–1119.
Dobó J. & Gyaraki R. (2021). A mesterséges intelligencia egyes felhasználási lehetőségei a rendvédelmi területeken [Some Uses of Artificial Intelligence in Law Enforcement]. Magyar Rendészet, 21(4), 67–81. https://doi.org/10.32577/mr.2021.4.3
Dror, I. E. & Morgan, R. M. (2019). A Futuristic Vision of Forensic Science. Journal of Forensic Sciences, 65(1), 239–242. https://doi.org/10.1111/1556-4029.14240
Dror, I. E. (2018). Biases in forensic experts. Science, 360(6386), 243–253. https://doi.org/10.1126/science.aat8443
Dror, I. E. (2013). What is (or will be) happening to the cognitive abilities of forensic experts in the new technological age. Journal of Forensic Sciences, 58(2), 563–572. http://dx.doi.org/10.1111/1556-4029.12079
Dror, I. E. & Mnookin, J. (2010). The use of technology in human expert domains: challenges and risks arising from the use of automated fingerprint identification systems in forensics. Law Probability Risk, 9(1), 47–67. http://dx.doi.org/10.1093/lpr/mgp031
el Rahwan, A. (2022). Artificial Intelligence and Interoperability for Solving Challenges of OSINT and Cross-Border Investigations. European Law Enforcement Research Bulletin, (6), 179–197.
Fazekas I. (2018). A mesterségesintelligencia-kutatás eredményei a kriminalisztika néhány vonatkozásában [The results of artificial intelligence research in some aspects of criminology]. Belügyi Szemle, 66(7-8), 55–65. https://doi.org/10.38146/BSZ.2018.7-8.4
Garfinkel, S. L. (2010). Digital forensics research: the next 10 years. Digital Investigation, 7(Suppl), 64–73. http://dx.doi.org/10.1016/j.diin.2010.05.009
Gelder, J., Vries, R.E., Demetriou, A., Sintemaartensdijk, I. & Donker, T. (2019). The virtual reality scenario method: moving from imagination to immersion in criminal decision-making research. Journal of Research in Crime and Delinquency, 56(3), 451–480. https://doi.org/10.1177/0022427818819696
Gosch, A. & Courtslow, C. (2019). On DNA transfer: the lack and difficulty of systematic research and how to do it better. Forensic Science International Genetics, 40, 24–36. http://dx.doi.org/10.1016/j.fsigen.2019.01.012
Hefetz, I. (2023). Mapping AI-ethics’ dilemmas in forensic case work: To trust AI or not? Forensic Science International, 350, 111807. https://doi.org/10.1016/j.forsciint.2023.111807
Herke Cs. (2021). A mesterséges intelligencia kriminalisztikai aspektusai [The forensic Aspects of Artificial Intelligence]. Belügyi Szemle, 69(10), 1709–1724. https://doi.org/10.38146/BSZ.2021.10.2
Ifa, D. R., Manicke, N. E., Dill, L. A. & Cooks, R. G. (2008). Latent fingerprint chemical imaging by mass spectrometry. Science, 321(805), 805–811. https://doi.org/10.1126/science.1157199
Kisfonai B. (2023). A bűnügyek jövőbeli megelőzése, avagy a prediktív rendészet új arca [Preventing crime in the future, or the new face of predictive policing]. Rendőrségi Tanulmányok, 3, 58–73. https://doi.org/10.53304/RT.2023.3.02
Koeijer, J., Sjerps, M., Vergeer, P. & Berger, C. (2019). Combining evidence in complex cases – a practical approach to interdisciplinary casework. Science & Justice, 60(1), 20–29. https://doi.org/10.1016/j.scijus.2019.09.001
Leese, M. (2022). Digital Data and Algorithms in Law Enforcement. European Law Enforcement Research Bulletin, (6), 39–46.
Leone, M. (2021). From Fingers to Faces: Visual Semiotics and Digital Forensics. International Journal of Semiotics Law, 34, 579–599. https://doi.org/10.1007/s11196-020-09766-x
Mátyás Sz., Mészáros B. & Szabó I. (2020). Prediktív rendészet [Predictive policing]. In Ruzsonyi P. (Szerk.), Közbiztonság: Fenntartható biztonság és társadalmi környezet tanulmányok III. (pp. 1895–2064). Nemzeti Közszolgálati Egyetem.
Morgan, R. M. (2019). Forensic science. The importance of identity in theory and practice. Forensic Science International: Synergy, 1, 239–242. https://doi.org/10.1016/j.fsisyn.2019.09.001
Morgan, R. M. & Levin, E. (2019). A crisis for the future of forensic science: lesson from the UK for the importance of epistemology for funding research and development. Forensic Science International: Synergy, 1, 243–252. https://doi.org/10.1016/j.fsisyn.2019.09.002
Neumann, C., Evett, I. W. & Skerrett, J. (2012). Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175(2), 1–26. https://doi.org/10.1111/j.1467-985X.2011.01027.x
Onen, M., Emond, N., Wang, B., Zhang, D., Ross, F. M., Li, J., Yildiz, B. & del Alamo, J. A. (2022). Nanosecond protonic programmable resistors for analog deep learning. Science, 377(6605), 539–543. https://doi:10.1126/science.abp8064
Oura, P., Junno, A. & Junno, J. A. (2021). Deep learning in forensic gunshot wound interpretation-a proof-of-concept study. International Journal of Legal Medicine, 135(5), 2101–2106. https://doi:10.1007/s00414-021-02566-3
Ozkaya, N. & Sagiroglu, S. (2010). Generating One Biometric Feature from Another: Faces from Fingerprints. Sensors, 10(5), 4206–4237. https://doi:10.3390/s100504206
Petrétei D. (2023). A daktiloszkópiai nyomkutatás modern módszerei I. – A fizikai módszerek [odern Methods of Latent Print Development I – Physical Methods]. Belügyi Szemle, 71(4), 585–601. https://doi.org/10.38146/BSZ.2023.4.2
Ribaux, O. & Talbot, W. B. (2014). Expanding forensic science through forensic intelligence. Science & Justice, 54(6), 494–501. http://dx.doi.org/10.1016/j.scijus.2014.05.001
Smolianitsky, E., Wolf, E. & Almog, J. (2014). Proactive forensic science: a novel class of cathinone precursors. Forensic Science International, 242, 219–227. https://doi.org/10.1016/j.forsciint.2014.06.020
Taylor, D., Biedermann, A., Samie, L., Pun, K. M., Hicks, T. & Champod, C. (2017). Helping to distinguish primary from secondary transfer events for trace DNA. Forensic Science International: Genetics, 28, 155–177. https://doi.org/10.1016/j.fsigen.2017.02.008
Zámpori A. (2021) A digitalizáció fejlődésének hatása a polgári perbeli bizonyításra [The impact of the development of digitalisation on evidence in civil proceedings]. Multidiszciplináris Tudományok, 11(5), 344–352 https://doi.org/10.35925/j.multi.2021.5.38
Zhou, Z. & Zare, R. N. (2017). Personal information from latent fingerprints using desorption electrospray ionization mass spectrometry and machine learning. Analytical Chemistry, 89(2), 1369–1372. https://doi.org/10.1021/acs.analchem.6b04498
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