Artificial Intelligence in Forensic Sciences: Bridging Systematic Challenges with Next-Generation Applications

Abstract

Forensic science has come a long way in the boost afforded by Artificial Intelligence (AI) that enabled the strengthening of forensic investigations in terms of accuracy, efficiency and automation. This research studies AI driven methods in important forensic domains like fingerprint and facial identification, cyber forensics, forensic toxicology, forensic imaging, etc. Four machine learning algorithms were implemented over forensic data to analyze data comprising Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forest and K-Nearest Neighbors (KNN). On test data, results of CNN achieved 96.8% accuracy on fingerprint recognition and SVM achieved 92.3% accuracy on facial identification. The Random Forest model achieved the accuracy of 89.5% in classifying the cyberforensic logs and 87.2% in forensic toxicology classification with the KNN model. A comparative analysis was made, in which it was observed that AI driven methods are more a faster, more precise and more automated than traditional forensic techniques. Also, blockchain integration offered security and integrity for the digital forensic evidence. However, the very fact of such advancements has introduced a number of ethical questions like bias, data privacy and forensic decision making. The focus of future research should be on obtaining transparent AI models, addressing issues of ethics, and integration of AI with new forensic technologies. This work shows that AI is well placed to solve systematic problems in forensic science, increasing accuracy and efficiency of investigations.