Book Title: Modern Forensic Tools and Devices: Trends in Criminal Investigation
Editors: Mr. Ravi Kumar, Ms. Nandini Katare, Don Caeiro, and Dr. Surbhi Girdhar
Chapter: 2
DOI: https://doi.org/10.59646/658/2
Author: Ms. Deepika Patel
Abstract
The integration of Artificial Intelligence and Machine Learning into forensic science is fundamentally transforming traditional methodologies, introducing enhanced levels of accuracy, efficiency, and reliability across numerous investigative domains (Kassem & Lodhi, 2024). This technological shift is poised to revolutionize forensic medicine and pathology by enabling sophisticated data analysis, pattern recognition, anomaly identification, and informed decision-making (Piraianu et al., 2023). These advancements extend to diverse applications such as forensic identification, ballistics analysis, traumatic injury assessment, postmortem interval estimation, and forensic toxicology. Moreover, the application of AI and ML facilitates comprehensive crime scene reconstruction, objective evaluation of medical acts, and the widespread adoption of virtual autopsy techniques, thereby significantly improving medico-legal practices and the overall effectiveness of forensic investigations. Despite these profound advancements, the integration of AI and ML in forensic science necessitates careful consideration of ethical implications, particularly concerning data privacy, potential algorithmic biases, and the interpretability of complex automated systems. Nonetheless, the proven ability of AI and ML to mitigate human subjectivity and reduce errors, alongside offering cost-effective solutions, underscores their critical role in the future trajectory of forensic science. This paradigm shift emphasizes the need for continuous oversight, rigorous validation, and robust ethical governance to ensure the integrity and justice-oriented application of these powerful technologies.