Publication Date
3-1-2024
Document Type
Article
Publication Title
Forensic Science International: Genetics
Volume
69
DOI
10.1016/j.fsigen.2023.102994
Abstract
Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.
Keywords
AI, Forensic DNA profiling, Human identification, Machine learning, STRs
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Justice Studies
Recommended Citation
Mark Barash, Dennis McNevin, Vladimir Fedorenko, and Pavel Giverts. "Machine learning applications in forensic DNA profiling: A critical review" Forensic Science International: Genetics (2024). https://doi.org/10.1016/j.fsigen.2023.102994
