Application of AI capabilities in forensics and criminal proceedings

Innovative technologies in forensic examinations

Autori

DOI:

https://doi.org/10.35295/osls.iisl.2555

Parole chiave:

artificial intelligence, deep learning, expert opinion, forensic examination

Abstract

This study aims to identify and systematically present the modern domains of forensic examination where AI capabilities can be effectively utilized. Additionally, the study intends to provide a general assessment of the applicability of AI tools to forensic science in the future, particularly regarding the potential expansion of their role. The findings suggest that AI has the potential to substantially enhance the toolkit of forensic examinations within the techno-biological spectrum, especially in areas requiring the processing of large datasets, sample comparison, and the identification of complex patterns. However, AI is less applicable in fields that require an understanding of human psychology, behavior, or event context. This study interests professionals in forensic science and forensic examination from a practical standpoint. Furthermore, it highlights which branches of forensic science may require human involvement in the future, offering insights into the implications for the labor market and education in this field.

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Biografia autore

Nursultan Poshanov, International University of Tourism and Hospitality, Humanitarian School

Corresponding author.

Riferimenti bibliografici

Ahmed Alaa El-Din, E., 2022. Artificial intelligence in forensic science: Invasion or revolution? Egyptian Society of Clinical Toxicology Journal [online], 10(2), 20–32. Available at: https://doi.org/10.21608/esctj.2022.158178.1012 DOI: https://doi.org/10.21608/esctj.2022.158178.1012

Aljanaahi, A., et al., 2025. A review of analytical and chemometric strategies for forensic classification of homemade explosives. Analytical Science Advances [online], 6(1), e70010. Available at: https://doi.org/10.1002/ansa.70010 DOI: https://doi.org/10.1002/ansa.70010

Alketbi, S. K., 2024. Emerging technologies in forensic DNA analysis. Perspectives in Legal and Forensic Sciences [online], 1(1), 1–24. Available at: https://doi.org/10.70322/plfs.2024.10007 DOI: https://doi.org/10.70322/plfs.2024.10007

Allied Market Research, 2022. Automated Fingerprint Identification Systems (AFIS) Market Outlook – 2030 [online] Available at: https://www.alliedmarketresearch.com/automated-fingerprint-identification-system-market-A12196

Andrić, S. D., and Ivanović, A. B., 2023. Forensic sciences and ethics in the era of application of artificial intelligence. Kriminalističke Teme [online], 23(3-4), 95–102. Available at: https://doi.org/10.51235/kt.2023.23.3-4.95 DOI: https://doi.org/10.51235/kt.2023.23.3-4.95

Bennett, M. R., and Budka, M., 2024. AI trained to recognise footprints, but it won’t replace forensic experts yet [online]. Available at: https://www.europeandissemination.eu/ai-trained-to-recognise-footprints-but-it-wont-replace-forensic-experts-yet/14799

Bock, T., Conolly, P., and Barroso, F., 2024. Why high-quality data is crucial to fighting financial crime [online]. 26 June. Available at: https://www.kroll.com/en/insights/publications/ai-financial-crime-prevention

Brodsky, S., 2023. Deepfake audio powered by AI can fool you—Here’s why you should care [online]. 7 August. Available at: https://www.lifewire.com/ai-powered-deepfake-audio-7570357

Chen, Y., and Tseng, P., 2023. The boundary of artificial intelligence in forensic science. Dialogo [online], 10(1), 83–90. Available at: https://doi.org/10.51917/dialogo.2023.10.1.5 DOI: https://doi.org/10.51917/dialogo.2023.10.1.5

Denning, P. J., and Arquilla, J., 2022. The context problem in artificial intelligence [online]. 1 December. Available at: https://cacm.acm.org/opinion/the-context-problem-in-artificial-intelligence/ DOI: https://doi.org/10.1145/3567605

Dunsin, D., et al., 2024. A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response. Forensic Science International: Digital Investigation [online], 48, 301675. Available at: https://doi.org/10.1016/j.fsidi.2023.301675 DOI: https://doi.org/10.1016/j.fsidi.2023.301675

Dunsin, D., Ghanem, M. C., and Ouazzane, K., 2022. The use of artificial intelligence in digital forensics and incident response (DFIR) in a constrained environment [online]. International Conference on Digital Forensics and Security of Cloud Computing (ICDFSCC). London Met Repository, 1–5. Available at: https://repository.londonmet.ac.uk/7708/

Dutta, S. K., et al., 2021. Study on enhanced deep learning approaches for value-added identification and segmentation of striation marks in bullets for precise firearm classification. Applied Soft Computing [online], 112, 107789. Available at: https://doi.org/10.1016/j.asoc.2021.107789 DOI: https://doi.org/10.1016/j.asoc.2021.107789

Europolygraph, 2024. The polygraph: A reliable tool for truth detection in the 21st century [online]. 27 September. Available at: https://europolygraph.org/en/the-polygraph-a-reliable-tool-for-truth-detection-in-the-21st-century/

Farber, S., 2025. AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows. Journal of Forensic Sciences [online], 70(3), 932–943. Available at: https://doi.org/10.1111/1556-4029.70035 DOI: https://doi.org/10.1111/1556-4029.70035

FBI, 2012. Privacy Impact Assessment Integrated Automated Fingerprint Identification System (IAFIS)/Next Generation Identification (NGI) Biometric Interoperability [online]. Available at: https://www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/freedom-of-information-privacy-act/department-of-justice-fbi-privacy-impact-assessments/iafis-ngi-biometric-interoperability

FBI, 2024. The integrated automated fingerprint identification system [online]. Available at: https://ucr.fbi.gov/fingerprints_biometrics/biometric-center-of-excellence/files/iafis_0808_one-pager825

Flinders University, 2024a. AI promises to ramp up PCR tests for faster DNA diagnostics and forensics [online]. 30 September. Available at: https://www.clinicallab.com/ai-promises-to-ramp-up-pcr-tests-for-faster-dna-diagnostics-and-forensics-28038

Flinders University, 2024b. AI used to upgrade DNA forensics [online]. 12 October. Available at: https://news.flinders.edu.au/blog/2024/10/12/ai-used-to-upgrade-dna-forensics/

Forensic Capability Network, 2024. AI can detect abusive messages 21 times faster than humans [online]. 16 July. Available at: https://www.fcn.police.uk/news/2024-07/ai-can-detect-abusive-messages-21-times-faster-humans

Galante, N., et al., 2023. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. International Journal of Legal Medicine [online], 137(2), 445–458. Available at: https://doi.org/10.1007/s00414-022-02928-5 DOI: https://doi.org/10.1007/s00414-022-02928-5

Gupta, R., et al., 2024. Voice disorder recognition using machine learning: A scoping review protocol. BMJ Open [online], 14(2), e076998. Available at: https://doi.org/10.1136/bmjopen-2023-076998 DOI: https://doi.org/10.1136/bmjopen-2023-076998

Gustafson, K., 2024. Modern forensic science technologies [online]. 7 January. Available at: https://www.forensicscolleges.com/blog/resources/10-modern-forensic-science-technologies

Hall, S. W., Sakzad, A., and Choo, K. R., 2022. Explainable artificial intelligence for digital forensics. WIREs Forensic Science [online], 4(2), e1434. Available at: https://doi.org/10.1002/wfs2.1434 DOI: https://doi.org/10.1002/wfs2.1434

Hamzah, N. H., et al., 2022. Artificial intelligence in forensic science: Current applications and future direction. Buletin Sains Kesihatan [online], 6(2), 39–46. Available at: https://myjms.mohe.gov.my/index.php/bsk/article/view/16561

Hamzelou, J., 2024. AI lie detectors are better than humans at spotting lies [online]. 5 July. Available at: https://www.technologyreview.com/2024/07/05/1094703/ai-lie-detectors-are-better-than-humans-at-spotting-lies/

Hartung, T., 2023. Artificial intelligence as the new frontier in chemical risk assessment. Frontiers in Artificial Intelligence [online], 6, 1269932. Available at: https://doi.org/10.3389/frai.2023.1269932 DOI: https://doi.org/10.3389/frai.2023.1269932

Hartung, T., and Kleinstreuer, N., 2025. Challenges and opportunities for validation of AI-based new approach methods. ALTEX - Alternatives to Animal Experimentation [online], 42(1), 3–21. Available at: https://doi.org/10.14573/altex.2412291 DOI: https://doi.org/10.14573/altex.2412291

HPCC Systems, 2024. Machine learning and the forensic application of audio classification [online]. Available at: https://hpccsystems.com/resources/machine-learning-and-the-forensic-application-of-audio-classification/

Hussain, D. A., and Jyotishana, J., 2024. The application of artificial intelligence in the forensic medicine and toxicology: Need of the hour. International Journal of Forensic Medicine [online], 6(2), 25–29. Available at: https://doi.org/10.33545/27074447.2024.v6.i2a.91 DOI: https://doi.org/10.33545/27074447.2024.v6.i2a.91

Icetana AI, 2024. Icetana AI are experts in AI CCTV Systems [online]. Available at: https://www.icetana.ai/ai-cctv-analytics

Industrial Cyber, 2025. Darktrace 2025 Report: AI threats surge, but cyber resilience grows amidst skills gap [online]. 5 March. Available at: https://industrialcyber.co/ai/darktrace-2025-report-ai-threats-surge-but-cyber-resilience-grows-amidst-skills-gap/

Jarrett, A., and Choo, K. K. R., 2021. The impact of automation and artificial intelligence on digital forensics. WIREs Forensic Science [online], 3(6), e1418. Available at: https://doi.org/10.1002/wfs2.1418 DOI: https://doi.org/10.1002/wfs2.1418

Karpuntsov, V., & Veresha, R. 2023. Legal Aspects of Virtual Assets Regulation in Ukraine. DANUBE, 14(3), 235–252. Available at: https://doi.org/10.2478/danb-2023-0014 DOI: https://doi.org/10.2478/danb-2023-0014

Kleinstreuer, N., and Hartung, T., 2024. Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*. Archives of Toxicology [online], 98, 735–754. Available at: https://doi.org/10.1007/s00204-023-03666-2 DOI: https://doi.org/10.1007/s00204-023-03666-2

Leno, S., and Hnot, T., 2024. Future of VQA: How multimodal AI is transforming video analysis [online]. 30 October. SoftServe. Available at: https://www.softserveinc.com/en-us/blog/multimodal-ai-for-video-analysis

Lim, A., 2024. TextOracle: Using AI to support forensic handwriting analysis [online]. HTX. 19 January. Available at: https://www.htx.gov.sg/whats-happening/all-news---events/all-news/2024/featured-news--textoracle--using-ai-to-support-forensic-handwriting-analysis

Maiti, D., and Das, D., 2022. Gender and hand identification based on dactyloscopy using deep convolutional neural network. In: A. K. Das et al., eds., Computational intelligence in pattern recognition: Proceedings of CIPR 2023 [online]. Cham: Springer, 145–155. Available at: https://doi.org/10.1007/978-981-99-3734-9_13 DOI: https://doi.org/10.1007/978-981-99-3734-9_13

Malwarebytes, 2024. II v kiberbezopasnosti — riski i vozmozhnosti [AI in cybersecurity: Understanding the risks] [online]. Available at: https://www.malwarebytes.com/ru/cybersecurity/basics/risks-of-ai-in-cyber-security

McDonald, C., et al., 2024. Developing a machine learning ‘smart’ polymerase chain reaction thermocycler part 2: Putting the theoretical framework into practice. Genes [online], 15(9), 1199. Available at: https://doi.org/10.3390/genes15091199 DOI: https://doi.org/10.3390/genes15091199

McShane, E. J., et al., 2022. Quantifying graphite solid-electrolyte interphase chemistry and its impact on fast charging. ACS Energy Letters [online], 7(8), 2734–2744. Available at: https://doi.org/10.1021/acsenergylett.2c01059 DOI: https://doi.org/10.1021/acsenergylett.2c01059

Mubarrat, A. A., 2024. Role of artificial intelligence and big data in modern forensic accountancy. Available at: https://doi.org/10.2139/ssrn.5078259 DOI: https://doi.org/10.2139/ssrn.5078259

Nagel, D., 2023. AI writing detection tool uses ‘forensic linguistic’ techniques to check authorship. THE Journal [online], 7 March. Available at: https://thejournal.com/articles/2023/03/07/ai-writing-detection-tool-uses-forensic-linguistic-techniques-to-check-authorship.aspx

Öztürk, S., Özyer, B., and Temiz, Ö., 2024. Detection of voices generated by artificial intelligence with deep learning methods. In: A. E. Çetin and E. Erdem, eds., 2024 32nd Signal Processing and Communications Applications Conference (SIU) [online]. Mersin: IEEE, 1–4. Available at: https://doi.org/10.1109/SIU61531.2024.10601078 DOI: https://doi.org/10.1109/SIU61531.2024.10601078

Parabon Nanolabs, 2024. The snapshot DNA phenotyping service [online]. Available at: https://snapshot.parabon-nanolabs.com/#phenotyping

Patsiouras, E., et al., 2024. Integrating AI and computer vision for ballistic and bloodstain analysis in 3D digital forensics. In: S. Livatino, P. Arpaia and L. T. De Paolis, eds., 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) [online]. St Albans: IEEE, 734–739. Available at: https://doi.org/10.1109/MetroXRAINE62247.2024.10796512 DOI: https://doi.org/10.1109/MetroXRAINE62247.2024.10796512

Prior, A., et al., 2025. Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning. Forensic Science International [online], 368, 112378. Available at: https://doi.org/10.1016/j.forsciint.2025.112378 DOI: https://doi.org/10.1016/j.forsciint.2025.112378

PwC, 2023. Impact of Artificial Intelligence on fraud and scams [online]. December. Available at: https://www.pwc.co.uk/forensic-services/assets/impact-of-ai-on-fraud-and-scams.pdf

Qureshi, S. M., et al., 2024. Deepfake forensics: A survey of digital forensic methods for multimodal deepfake identification on social media. PeerJ Computer Science [online], 10, e2037. Available at: https://doi.org/10.7717/peerj-cs.2037 DOI: https://doi.org/10.7717/peerj-cs.2037

Raghav, A., et al., 2025. Forensic ballistics and artificial intelligence: Legal challenges and enhancing international framework for scientific evidence. In: C. Kaunert, ed., Forensic intelligence and deep learning solutions in crime investigation [online]. Hershey: IGI Global, 163–182. Available at: https://doi.org/10.4018/979-8-3693-9405-2.ch009 DOI: https://doi.org/10.4018/979-8-3693-9405-2.ch009

Rawat, R., et al., 2023. Autonomous artificial intelligence systems for fraud detection and forensics in dark web environments. Informatica [online], 47(9), 51–62. Available at: https://doi.org/10.31449/inf.v46i9.4538 DOI: https://doi.org/10.31449/inf.v46i9.4538

Renukadevi, P., John, S., and Shivani, N., 2024. Forensic Science: AI-Powered Image and Audio Analysis. 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) [online]. Trichy: IEEE, 1519–1525. Available at: https://doi.org/10.1109/icosec61587.2024.10722068 DOI: https://doi.org/10.1109/ICOSEC61587.2024.10722068

Rizvi, S., et al., 2022. Application of artificial intelligence to network forensics: Survey, challenges and future directions. IEEE Access [online], 10, 110362–110384. Available at: https://doi.org/10.1109/ACCESS.2022.3214506 DOI: https://doi.org/10.1109/ACCESS.2022.3214506

Sessa, F., et al., 2024. Artificial intelligence and forensic genetics: Current applications and future perspectives. Applied Sciences [online], 14(5), 2113. Available at: https://doi.org/10.3390/app14052113 DOI: https://doi.org/10.3390/app14052113

Siegel, D., et al., 2022. Forensic data model for artificial intelligence based media forensics-Illustrated on the example of DeepFake detection. Electronic Imaging [online], 34, 1–6. Available at: https://doi.org/10.2352/EI.2022.34.4.MWSF-324 DOI: https://doi.org/10.2352/EI.2022.34.4.MWSF-324

Sisodia, N., 2025. Artificial Intelligence in Forensic Toxic Science: Emerging Trends and Analytical Techniques. International Journal for Research in Applied Science and Engineering Technology [online], 13(9), 1571–1579. Available at: https://doi.org/10.22214/ijraset.2025.74270 DOI: https://doi.org/10.22214/ijraset.2025.74270

Snohomish County, 2022. Suspect identified in 1990 homicide cold case [online]. 30 June. Available at: https://www.snohomishcountywa.gov/Archive.aspx?ADID=6870

Sparkes, M., 2021. AI clears up images of fingerprints to help with identification. New Scientist [online], 28 June. Available at: https://www.newscientist.com/article/2282218-ai-clears-up-images-of-fingerprints-to-help-with-identification/

Starke, G., D’Imperio, A., and Ienca, M. 2023. Out of their minds? Externalist challenges for using AI in forensic psychiatry. Frontiers in Psychiatry [online], 14, 1209862. Available at: https://doi.org/10.3389/fpsyt.2023.1209862 DOI: https://doi.org/10.3389/fpsyt.2023.1209862

Taylor, M., Osborne, N., and Waltke, H., 2024. Artificial Intelligence (AI) in forensic science use case explorer [online]. National Institute of Standards and Technology (NIST). Available at: https://forensicrti.org/wp-content/uploads/2024/10/Artificial-Intelligence-AI-in-Forensic-Science-Use-Case-Explorer.pdf

Thales, 2026. CABIS. The next generation of ABIS [online]. Available at: https://www.thalesgroup.com/en/solutions-catalogue/public-security/national-security/cabis

The IoT Academy, 2024. Deepfake detection technology – Working/uses/tools [online]. 20 September. Available at: https://www.theiotacademy.co/blog/deepfake-detection/

US Bureau of Labor Statistics, 2024. Forensic science technicians [online]. Available at: https://www.bls.gov/ooh/life-physical-and-social-science/forensic-science-technicians.htm

US Department of Homeland Security, 2023. Leveraging artificial intelligence is smart for explosive detection [online]. 14 December. Available at: https://www.dhs.gov/science-and-technology/news/2023/12/14/feature-article-leveraging-artificial-intelligence-smart-explosive-detection

Valid8, 2024. The impact of AI on fraud investigations: What forensic accountants need to know [online]. Available at: https://www.valid8financial.com/guides/m/ai-and-fraud-investigations

Veresha, R. 2017. Criminal and legal characteristics of criminal intent. Journal of financial crime, 24(1), 118–128. Available at: https://doi.org/10.1108/JFC-02-2016-0013 DOI: https://doi.org/10.1108/JFC-02-2016-0013

Wang, J., et al., 2023. Breaking boundaries between linguistics and artificial intelligence. Journal of Organizational and End User Computing [online], 35(1), 1–20. Available at: https://doi.org/10.4018/joeuc.334013 DOI: https://doi.org/10.4018/JOEUC.334013

Wankhade, T. D., et al., 2022. Artificial intelligence in forensic medicine and toxicology: the future of forensic medicine. Cureus [online], 14(8), 28376. Available at: https://doi.org/10.7759/cureus.28376 DOI: https://doi.org/10.7759/cureus.28376

Zhao, H., and Li, H., 2023. Handwriting identification and verification using artificial intelligence-assisted textural features. Scientific Reports [online], 13(1), 21739. Available at: https://doi.org/10.1038/s41598-023-48789-9 DOI: https://doi.org/10.1038/s41598-023-48789-9

Pubblicato

2026-07-30

Come citare

Bakyt, S. (2026) «Application of AI capabilities in forensics and criminal proceedings: Innovative technologies in forensic examinations», Oñati Socio-Legal Series, 16(4), pp. 1844–1866. doi: 10.35295/osls.iisl.2555.