# Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives
## 4 Discussion  
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]. 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]. To reduce AI bias, several strategies could be applied, such as robust data augmentation for each algorithm used, the application of counterfactual fairness, and the imperative for diverse, representative datasets alongside unbiased data collection methods [60]. Moreover, each tool must report its known limitations. As previously described, an important step is represented by the definition of ML algorithms. In this sense, ANNs are networks that, by employing a condensed set of ideas from biological neural systems, mimic a biological neural network. ANNs simulate the electrical activity of the brain and nervous system: there are neurodes arranged as a layer or vector: each output represents an input for the following layers. To date, in the forensic context, ANN applications have been useful in criminal detection through the comparison of faces [61]. Another algorithm is the Random Forest which operates by constructing a multitude of decision trees based on samples, their variables, and their class. An example of its application is the age estimation of bloodstains based on temporal colorimetric analysis. The SVM is an ML algorithm that determines boundaries between data points based on predefined classes, labels, or outputs [62]. It uses supervised learning models to solve complex problems related to classification, regression, and outlier detection. In forensic sciences, it is frequently used in anthropology studies [63]. Another algorithm that could be used for ML is XGBoost; it belongs to the ensemble learning category, specifically the gradient boosting framework, using decision trees as base learners and employing regularization techniques to enhance model generalization. This algorithm could be used in a forensic context to evaluate biogeographic ancestry [56]. A fundamental step is the evaluation of the algorithm used for the ML. For example, the classification accuracy represents the ratio of the total number of input samples to the number of accurate predictions; it is used to evaluate the accuracy. Another method used for algorithm evaluation is the Area Under Curve (AUC). It is used for binary classification problems: the likelihood that a randomly selected positive example will be ranked higher than a randomly selected negative example is known as the AUC of a classifier. The F1 Score represents the harmonic mean of memory and accuracy. The F1 Score is used to provide two key performance indicators and it has a range of (0, 1). The indicators are the number of examples that the classifier properly classifies (its precision) and the number of instances that it does not miss (its robustness) [64; 65]. Different genomics and genetics issues have been addressed by ML techniques, such as functional gene annotations, pattern recognition in DNA sequences, and annotation of genomic sequence elements. Histone modification, transcription factor (TF) binding ChIP-seq data, chromatin accessibility assays, microarray or RNA-seq expression data, and other genomic assay data can all be fed into ML. Gene expression data can be utilized to find potentially useful illness biomarkers and differentiate between various disease characteristics.