THE FORECASTING DIMENSION OF THE IMPACT OF DIGITAL TECHNOLOGIES ON THE CAPABILITIES OF CERTAIN GENERA (SPECIES) OF FORENSIC EXAMINATIONS
Abstract and keywords
Abstract:
The article attempts to assess the possibilities of the influence of particular digital technologies on the methods and techniques of forensic examinations. It is pointed out that, given the complexity of assessing the scientific validity, practical applicability and permissibility of using certain technologies in forensic examinations, there is a high risk of distorted perception and exaggeration of the possibilities of innovative technologies by officials and authorities, who appoint examinations. This requires a review of certain relevant solutions, among which 3D technologies, unmanned aerial vehicles, biometric technologies and artificial intelligence are noted in the work. The paper identifies specific genera and species of forensic examinations, the development of which these technologies can affect most strongly. A number of related prospects and problems are outlined, as well as the possibility of further resolving the issues. Through the prism of forecasting, the vectors of development of forensic activities are discussed, taking into account the introduction of new technologies. The above can also serve as guiding information for the law enforcement agencies in the process of understanding trends in the development of forensic expertise.

Keywords:
FORENSIC SCIENCE, FORENSIC FORECASTING, PROGNOSTIC INFORMATION, FORECASTING, GENERA AND SPECIES OF FORENSIC EXAMINATIONS, DIGITALIZATION, METHODOLOGY OF FORENSIC EXAMINATIONS
Text
Text (PDF): Read Download
References

1. Rossinskaya E. R. Epistemological and activity expert errors when using modern technologies in the production of forensic examinations. Vestnik of Moscow University of the Ministry of Internal Affairs of Russia, 18-22, 2015. (In Russ.).

2. Polyakova A. V. Formation and development of 3D technologies in forensic activities: methodological and organizational aspects. Dissertation of candidate of juridical sciences. Ufa; 2023: 244. (In Russ.).

3. Carew R. M., Errickson D. An overview of 3D printing in forensic science: the tangible third-dimension. Journal of Forensic Sciences, 1754, 2020. (In Eng.).

4. Jani G., Johnson A., Marques J., Franco A. Three-dimensional (3D) printing in forensic science - an emerging technology in India. Annals of 3D printed medicine, 2021. Available from: https://www.sciencedirect.com/science/article/pii/S2666964121 000011. Accessed: 29 May 2024. (In Eng.).

5. Carew R. M. (et al.) 3D forensic science: a new field integrating 3D imaging and 3D printing in crime reconstruction. Forensic Sci. Int. Synergy, 2021. Available from: https://www.sciencedirect.com/science/article/pii/S2589871X21000759?via%3Dihub. Accessed: 29 May 2024. (In Eng.).

6. Tolstolutsky V. Yu., Malichenko V. V. 3D modeling as a way of non-verbal presentation of the results of transport and technological expertise. In: National and international trends and prospects for the development of forensic expertise. Compendium of a scientific and practical conference with international participation, Nizhny Novgorod, 22-23 May 2024. Nizhny Novgorod: UNN; 2024: 367-372. (In Russ.).

7. Lepeshkin D. A., Shakhtarin E. A. The use of mobile scanning tools when examining the scene of a traffic accident. In: National and international trends and prospects for the development of forensic expertise. Compendium of a scientific and practical conference with international participation, Nizhny Novgorod, 22-23 May 2024. Nizhny Novgorod: UNN; 2024: 213-219. (In Russ.).

8. Mailis N. P. The role of innovative technologies in the development of digital tracology. Theory and practice of forensic examination, 18-22, 2022. (In Russ.).

9. Nedobitkov A. I. Digital transport traceology based on Agisoft metascape and unmanned aerial vehicle. Vestnik SibADI, 890-899, 2022. (In Russ.).

10. Musina G. A., Ozhigin D. S., Ozhigina S. B. Environmental monitoring based on images obtained using unmanned aerial vehicles. Interexpo Geo-Siberia. 196-204, 2019. (In Russ.).

11. Sudhakar S., Vijayakumar V., Sathiya Kumar C. (et al.) Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires. Computer Communications, 2020. (In Eng.). DOI:https://doi.org/10.1016/j.comcom.2019.10.007

12. Rassolov I. M., Chubukova S. G., Makarova I. V. Biometrics in the context of personal data and genetic information: legal problems. Lex Russica, 108-118, 2019. (In Russ.).

13. Sabanov A. G., Smolina S. G. Comparative analysis of biometric identification methods. Proceedings of the ISA RAS, 11-20, 2016. (In Russ.).

14. Khaziev Sh. N. Criminalistic and forensic foundations of modern biometric technologies. Theory and practice of forensic examination, 16-21, 2023. (In Russ.).

15. Zinin A. M. Identification of a person by signs of appearance and methods of biometrics. Courier of the Kutafin Moscow State Law University (MSAL), 58-66, 2022. (In Russ.).

16. Bakhteev D. V. Big data and artificial intelligence in investigative and expert activities. In: Actual problems of criminalistics and forensic examination. Compendium of the International scientific and practical conference. Irkutsk: East Siberian Institute of the Ministry of Internal Affairs of Russia; 2019: 104-107. (In Russ.).

17. Kupin A. F., Kovalenko A. S. On the question of the possibilities of using artificial intelligence systems in the forensic examination of documents and their details. Theory and practice of forensic examination, 28-35, 2023. (In Russ.).

18. Bessonov A. A. Artificial intelligence and mathematical statistics in the criminalistic study of crimes. Monograph. Moscow: Prospekt; 2021: 816. (In Russ.).

19. Bakhteev D. V. Conceptual foundations of the theory of criminalistic thinking and the use of artificial intelligence systems in the investigation of crimes. Dissertation of doctor of juridical sciences. Yekaterinburg; 2022: 504. (In Russ.).

20. Srihari S. N., Srinivasan H., Chen S., Beal M. J. Machine learning for signature verification. In: Machine learning in document analysis and recognition. Eds.: S. Marinai, H. Fujisawa. Berlin; Heidelberg: Springer; 2008: 387-408. (Studies in Computational Intelligence. Vol. 90). Available from:https://doi.org/10.1007/978-3-540-76280-5_15. Accessed: 07.06.2024. (In Eng.).

Login or Create
* Forgot password?