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: 22
DOI: https://doi.org/10.59646/658/22
Author: Ms. Reshma M Ashok
Abstract
The integration of artificial intelligence and advanced computational methods into forensic science has profoundly reshaped the landscape of evidence analysis, offering enhanced efficiency and expanded investigative capabilities (Meghwal, 2026). This technological evolution, however, introduces complex challenges concerning the reliability and admissibility of AI-generated forensic evidence in legal proceedings. Specifically, the “black box” nature of many AI algorithms, where the internal workings and decision-making processes remain opaque, presents significant hurdles for legal frameworks that demand transparency and explainability for evidence presented in court. This opaqueness necessitates a robust judicial scrutiny to ensure that such evidence adheres to established standards of scientific validity and avoids infringing upon fundamental legal principles such as the presumption of innocence. . The lack of accessibility for laypersons, coupled with the highly technical nature of AI code, further exacerbates concerns regarding the detection of inherent flaws or biases within these systems. Consequently, the judicial system must develop sophisticated frameworks for evaluating the evidentiary software, enabling robust adversarial scrutiny and ensuring the transparency and validity of AI outputs in legal contexts. This chapter will delve into the critical aspects of admissibility criteria for advanced forensic evidence, examining how legal precedents and scientific standards intersect in evaluating AI-driven analytical outcomes (Stone & Vaidyan, 2025). It will also explore the challenges posed by machine-generated evidence, particularly when offered without direct expert testimony, and consider proposed amendments to evidentiary rules designed to address these emergent forms of forensic information. It will further explore the evolving role of the forensic expert, who, in this AI-driven era, transitions from a sole interpreter of material traces to a crucial mediator between complex algorithmic outputs and the established legal standards governing admissible evidence. This necessitates an examination of how expert witnesses can effectively articulate the underlying methodologies and limitations of AI models to triers of fact, ensuring that the probative value of such evidence is properly understood and weighed against potential prejudices.