Beyond the Hype: Limits and Opportunities of Artificial Intelligence in Forensic Science

Artificial intelligence is rapidly entering forensic practice, promising objective analysis and enhanced capabilities across multiple disciplines. However, the forensic community faces critical questions: Which AI applications offer genuine value? What are their fundamental limitations? How do we validate AI tools for forensic use? This presentation addresses these questions through scientific principles and practical case studies.

The talk examines the current landscape of AI in forensics, distinguishing between discriminative AI (pattern recognition and classification) and generative AI (content creation), each with distinct capabilities and pitfalls. Generative AI introduces inherent biases and fabrication risks that make it unsuitable for evidentiary analysis, while discriminative approaches show promise when properly validated and applied within their limitations.

Using bloodstain pattern classification as a detailed case study, the presentation demonstrates both the potential and constraints of AI in forensic analysis. A discriminative learning system trained on experimental data achieves 80-90% accuracy in identifying pattern types and reconstructing mechanisms—a substantial improvement over subjective visual assessment, yet far from infallible. This example illustrates broader principles applicable across forensic disciplines: the necessity of validation with ground-truth data, quantification of error rates, transparency in methodology, and integration with traditional expertise rather than replacement.