Artificial Intelligence in Forensic Science: Applications, Legal Framework and Criminal Liability Regime
Esra Nur Bal, Aylin Yalçın Sarıbey · 2025
The forensic science literature increasingly underscores the limitations of certain conventional methodologies, citing concerns over their insufficient empirical validation, vulnerability to human error and cognitive bias, methodological inconsistencies in evidence collection, protracted processing durations, inefficiencies in handling large-scale datasets, lack of procedural standardization, and the analytical challenges posed by degraded or complex evidentiary material. This study investigates how the integration of artificial intelligence (AI) into forensic sciences may contribute to overcoming the structural and operational challenges inherent in traditional forensic methodologies. It examines the implementation and contributions of AI-driven applications across diverse forensic domains, including forensic pathology, digital forensics, forensic anthropology, and crime scene investigation. In doing so, it critically engages with challenges such as algorithmic bias, transparency and the reliability which are essential considerations for the judicial acceptance of AI-generated evidence, and issues of criminal liability in AI-driven offenses, while also addressing the relevance of existing legal frameworks—most notably the European Union’s Artificial Intelligence Act—and the pressing need for future regulatory advancements. The article concludes by emphasizing the importance of training, robust oversight mechanisms, and the development of international standards for the effective, ethical, and secure integration of AI in forensic sciences, offering recommendations to ensure justice and technological progress.