Machine learning applications in forensic DNA profiling: A critical review

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

“…AI techniques, such as ML, data mining, and pattern recognition, can be effectively employed to analyze genetic data and facilitate the identification of suspects. These technologies enable forensic experts to process and obtain valuable results from vast amounts of data, including fingerprints, DNA samples, and surveillance footage, facilitating faster and more accurate identification of suspects [40]. Moreover, AI-driven facial recognition systems assist in identifying criminals from images or videos, assisting law enforcement agencies in solving crimes and bringing justice to victims [40].…”

“…These technologies enable forensic experts to process and obtain valuable results from vast amounts of data, including fingerprints, DNA samples, and surveillance footage, facilitating faster and more accurate identification of suspects [40]. Moreover, AI-driven facial recognition systems assist in identifying criminals from images or videos, assisting law enforcement agencies in solving crimes and bringing justice to victims [40]. There are limitations and potential biases that may arise when utilizing AI algorithms in forensic genetics, and, as such, emphasis must be placed on the importance of transparency, interpretability, and fairness in algorithmic decision making [59].…”

“…Gene expression data can be utilized to find potentially useful illness biomarkers and differentiate between various disease characteristics. It may be possible to identify new types of functional elements by using chromatin data to annotate the genome in an "unsupervised" manner [40].…”

“…The integration of AI algorithms and forensic genetics has the potential to enhance the accuracy, efficiency, and reliability of forensic investigations, ultimately leading to better outcomes in solving crimes and identifying perpetrators [40]. The seminal paper that focused on the application of AI to forensic genetics was published in 2008 [41].…”

“…Complex analyses are needed for the processing of generated data, and these tasks cannot be completed without computational support [44]. Current applications of ML in forensic biology can be divided into three categories: applications related to human identification, applications related to forensic intelligence, and applications related to increasing the evidential value of DNA evidence [40].…”