Artificial intelligence is reshaping how forensic scientists analyze biological evidence, making crime scene investigations faster, more reliable, and more scientifically defensible. The Natural Traces Consortium, a network of 11 research institutions across Europe, is integrating AI systems with cutting-edge biological analysis to expand the types of evidence that can solve crimes, from insect colonies on bodies to microbial signatures in soil.

What Biological Evidence Can AI Help Forensic Scientists Analyze?

Traditional forensic science has long relied on fingerprints and DNA profiles, but biological evidence extends far beyond human genetic material. The Natural Traces Consortium is developing AI-powered methods to interpret a much wider range of biological traces that can help reconstruct what happened at a crime scene.

• Insect Evidence: Researchers at Goethe University Frankfurt analyze insects found on decomposing bodies to estimate time since death, even when discovery occurs weeks or months later. They examine 100 to 150 insect-infested bodies annually, using advanced biology and chemistry to reconstruct colonization timelines.
• Environmental DNA: Scientists at Eötvös Loránd University in Budapest use long-read metagenomics to identify mixed biological traces, such as non-human DNA from animals or plants at a crime scene. This approach helps interpret complex evidence samples that traditional methods struggle to analyze.
• Microbial Signatures: Researchers at the University of Potsdam study microbial communities to infer the geographic origin of biological samples. By combining DNA sequencing with machine learning models, they can help determine where a suspect or victim may have been before arriving at a crime scene.
• Fungal Communities: Scientists at the University of Genoa investigate fungi on bodies and materials to support timing and environmental inference, developing standardized protocols and growth tables for forensic interpretation.
• Pollen and Plant Traces: Researchers at the University of Modena and Reggio Emilia improve pollen sampling and identification to enable high-resolution environmental attribution, helping link suspects or victims to specific locations.

How Are AI Systems Making Forensic Evidence More Reliable in Court?

One of the biggest challenges in forensic science is ensuring that evidence interpretation is consistent, transparent, and legally defensible. Courts increasingly demand that forensic conclusions be based on rigorous statistical frameworks rather than subjective expert judgment. AI is helping solve this problem by automating the calculation of likelihood ratios, a statistical measure that quantifies how much a piece of evidence supports one hypothesis over another.

Researchers at Universidad Autonoma de Madrid are developing AI and statistical systems that compute likelihood ratios in compliance with ISO 21043, an international standard for forensic evidence evaluation. These systems create rigorous validation protocols and adaptable solutions across different forensic domains, ensuring that forensic reporting is precise, reliable, interpretable, and defensible across diverse legal systems.

Steps to Strengthen Forensic Evidence Interpretation with AI

• Standardize Statistical Frameworks: Implement likelihood ratio calculations aligned with international standards to ensure consistent, defensible evidence evaluation across jurisdictions and case types.
• Develop Comprehensive Reference Datasets: Build large, diverse databases of biological signatures, such as fungal growth patterns, diatom species distributions, and microbial communities, to improve the accuracy of AI models trained on real-world evidence.
• Validate AI Models Rigorously: Test AI systems on known samples and establish clear performance benchmarks before deploying them in criminal investigations to ensure reliability and minimize false positives or negatives.
• Train Forensic Scientists in AI Literacy: Ensure that forensic professionals understand how AI systems work, what their limitations are, and how to interpret their outputs in a way that is transparent to judges and juries.

What Specific Forensic Challenges Is AI Helping to Solve?

Mixed biological traces are among the most difficult types of evidence to interpret. A crime scene might contain DNA from multiple people, animals, and environmental sources all mixed together, making it nearly impossible to separate using traditional methods. Researchers at the University of Porto are developing computational methods that use machine learning and deep learning models within a Bayesian inference framework to analyze microbiome and environmental DNA data. These systems integrate preprocessing pipelines, feature engineering, and synthetic data generation to enable robust, interpretable quantification of mixed biological traces.

Another challenge is identifying biological material from degraded or processed samples. Researchers at Charles University in Prague develop DNA barcoding and STR multiplexing systems to detect protected species in materials like tanned hides and traditional medicine artifacts. They also explore portable technologies, including Bento Lab and LAMP assays, to support rapid, field-based analysis that could eventually bring forensic capabilities directly to crime scenes.

Drowning cases present unique forensic challenges because the cause of death can be difficult to determine. Researchers at Tallinn University of Technology use diatoms, microscopic algae with species-specific environmental preferences, to diagnose post-mortem drowning and reconstruct drowning sites. By integrating expertise from paleoecology with forensic methodologies, they optimize sampling strategies and develop comprehensive reference datasets to standardize the use of aquatic microorganisms as reliable forensic indicators.

How Does Air Forensics Add a New Dimension to Crime Scene Investigation?

A relatively new approach called air forensics captures environmental DNA from the air itself, before it settles on surfaces or becomes part of dust and soil. Researchers at Universidad Nacional de San Juan collect environmental DNA in indoor and outdoor settings to reconstruct crime events. This approach integrates human and non-human environmental DNA trace analysis, providing current information about people, wildlife, and environmental context that complements established forensic disciplines.

The integration of these diverse biological and AI-driven approaches represents a fundamental shift in forensic science. Rather than relying on a single type of evidence, investigators can now draw on multiple independent lines of biological evidence, each analyzed using AI systems that provide statistically rigorous, legally defensible conclusions. As these methods mature and become more widely adopted, they have the potential to increase the accuracy of forensic investigations, reduce wrongful convictions, and help solve cold cases that traditional methods could not crack.