{"id":22026,"date":"2025-03-16T16:31:54","date_gmt":"2025-03-16T15:31:54","guid":{"rendered":"https:\/\/www.forenseek.fr\/ia-en-criminalistique-entre-revolution-technologique-et-defis-humains\/"},"modified":"2025-11-10T11:01:18","modified_gmt":"2025-11-10T10:01:18","slug":"ia-en-criminalistique-entre-revolution-technologique-et-defis-humains","status":"publish","type":"post","link":"https:\/\/www.forenseek.fr\/en\/ia-en-criminalistique-entre-revolution-technologique-et-defis-humains\/","title":{"rendered":"AI in Forensics: Between Technological Revolution and Human Challenges"},"content":{"rendered":"

By Yann CHOVORY, Engineer in AI Applied to Criminalistics (Institut G\u00e9n\u00e9tique Nantes Atlantique \u2013 IGNA). On a crime scene, every minute counts. Between identifying a fleeing suspect, preventing further wrongdoing, and managing the time constraints of an investigation, case handlers are engaged in a genuine race against the clock. Fingerprints, gunshot residues, biological traces, video surveillance, digital data\u2026 all these clues must be collected and quickly analyzed, or there is a risk that the case will collapse for lack of usable evidence in time. Yet overwhelmed by the ever-growing mass of data, forensic laboratories are struggling to keep pace.<\/strong><\/h3>

Analyzing evidence with speed and accuracy<\/strong>
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In this context, artificial intelligence (AI) establishes itself as an indispensable accelerator. Capable of processing in a few hours what would take weeks to analyze manually, it optimises the use of clues by speeding up their sorting and detecting links imperceptible to the human eye. More than just a time-saver, it also improves the relevance of investigations: swiftly cross-referencing databases, spotting hidden patterns in phone call records, comparing DNA fragments with unmatched precision. AI thus acts as a tireless virtual analyst, reducing the risk of human error and offering new opportunities to forensic experts.<\/p>

But this technological revolution does not come without friction. Between institutional scepticism and operational resistance, its integration into investigative practices remains a challenge. My professional journey, marked by a persistent quest to integrate AI into scientific policing, illustrates this transformation\u2014and the obstacles it faces. From a marginalised bioinformatician to project lead for AI at IGNA, I have observed from within how this discipline, long grounded in traditional methods, is adapting\u2014sometimes under pressure\u2014to the era of big data.<\/p>

The risk of human error is reduced and the reliability of identifications increased
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Concrete examples: AI from the crime scene to the laboratory<\/strong><\/strong><\/h2>

AI is already making inroads in several areas of criminalistics, with promising results. For example, AFIS (Automated Fingerprint Identification System) fingerprint recognition systems now incorporate machine learning components to improve matching of latent fingerprints. The risk of human error is reduced and the reliability of identifications increased [1]. Likewise, in ballistics, computer vision algorithms now automatically compare the striations on a projectile with markings of known firearms, speeding the work of a firearms expert. Tools are also emerging to interpret bloodstains on a scene: machine learning1<\/sup> models can help reconstruct the trajectory of blood droplets and thus the dynamics of an assault or violent event [2]. These examples illustrate how AI is integrating into the forensic expert\u2019s toolkit, from crime scene image analysis to the recognition of complex patterns.<\/strong>But it is perhaps in forensic genetics that AI currently raises the greatest hopes. DNA analysis labs process thousands of genetic profiles and samples, with deadlines that can be critical. AI offers a considerable time-gain and enhanced accuracy. As part of my research, I contributed to developing an in-house AI capable of interpreting 86 genetic profiles in just three minutes [3]\u2014a major advance when analyzing a complex profile may take hours. Since 2024, it has autonomously handled simple profiles, while complex genetic profiles are automatically routed to a human expert, ensuring effective collaboration between automation and expertise. The results observed are very encouraging. Not only is the turnaround time for DNA results drastically reduced, but the error rate also falls thanks to the standardization introduced by the algorithm.<\/strong><\/p>

AI does not replace humans but complements them<\/strong><\/p><\/blockquote>

Another promising advance lies in enhancing genetic DNA-based facial composites. Currently, this technique allows estimating certain physical features of an individual (such as eye color, hair color, or skin pigmentation) from their genetic code, but it remains limited by the complexity of genetic interactions and uncertainties in predictions. AI could revolutionise this approach by using deep learning models trained on vast genetic and phenotypic databases, thereby refining these predictions and generating more accurate sketches. Unlike classical methods, which rely on statistical probabilities, an AI model could analyse millions of genetic variants in a few seconds and identify subtle correlations that traditional approaches do not detect<\/strong>. This prospect opens the way to a significant improvement in the relevance of DNA sketches, facilitating suspect identification when no other usable clues are available. The Forenseek platform has explored current advances in this area, but AI has not yet been fully exploited to surpass existing methods [5]. Its integration could therefore constitute a major breakthrough in criminal investigations.<\/p>

It is important to emphasize that in all these examples, AI does not replace the human but complements them. At IRCGN (French National Gendarmerie Criminal Research Institute) cited above, while the majority of routine, good-quality DNA profiles can be handled automatically, regular human quality control remains: every week, a technician randomly checks cases processed by AI, to ensure no drift has occurred [3]. This human-machine collaboration is key to successful deployment, as the expertise of the forensic specialists remains indispensable to validate and finely interpret the results, especially in complex cases.<\/strong><\/p>

\"Intelligence<\/figure>

Algorithms Trained on Data: How AI \u201cLearns\u201d in Forensics<\/strong><\/strong><\/h2>

The impressive performance of AI in forensics relies on one crucial resource: data. For a machine learning algorithm to identify a fingerprint or interpret a DNA profile, it first needs to be trained on numerous examples. In practical terms, we provide it with representative datasets, each containing inputs (images, signals, genetic profiles, etc.) associated with an expected outcome (the identity of the correct suspect, the exact composition of the DNA profile, etc.). By analyzing thousands\u2014or even millions\u2014of these examples, the machine adjusts its internal parameters to best replicate the decisions made by human experts. This is known as supervised learning<\/strong>, since the AI learns from cases where the correct outcome is already known. For example, to train a model to recognize DNA profiles, we use data from solved cases where the expected result is clearly established.<\/p>

an AI\u2019s performance depends on the quality of the data that trains it.<\/em><\/strong><\/p><\/blockquote>

The larger and more diverse the training dataset, the better the AI will be at detecting reliable and robust patterns. However, not all data is equal. It must be of high quality (e.g., properly labeled images, DNA profiles free from input errors) and cover a wide enough range of situations. If the system is biased by being exposed to only a narrow range of cases, it may fail when confronted with a slightly different scenario. In genetics, for instance, this means including profiles from various ethnic backgrounds, varying degrees of degradation, and complex mixture configurations so the algorithm can learn to handle all potential sources of variation.<\/p>

Transparency in data composition is essential. Studies have shown that some forensic databases are demographically unbalanced\u2014for example, the U.S. CODIS database contains an overrepresentation of profiles from African-American individuals compared to other groups [6]. A model naively trained on such data could inherit systemic biases and produce less reliable or less fair results for underrepresented populations. It is therefore crucial to monitor training data for bias and, if necessary, to correct it (e.g., through balanced sampling, augmentation of minority data) in order to achieve fair and equitable learning.<\/p>

\"\"
Data Collection<\/strong>: Gathering diverse and representative datasets
Data Preprocessing<\/strong>: Cleaning and preparing data for training
AI Training<\/strong>: Training algorithms on prepared datasets
Data Validation<\/strong>: Verifying the quality and diversity of the data
Bias Evaluation<\/strong>: Identifying and correcting biases in the datasets<\/em><\/figcaption><\/figure>

Technically, training an AI involves rigorous steps of cross-validation and performance measurement.<\/strong> We generally split data into three sets: one for training, another for validation during development (to adjust the parameters), and a final test set to objectively evaluate the model. Quantitative metrics such as accuracy, recall (sensitivity), or error curves make it possible to quantify how reliable the algorithm is on data it has never seen [6]. For example, one can check that the AI correctly identifies a large majority of perpetrators from traces while maintaining a low rate of false positives. Increasingly, we also integrate fairness and ethical criteria into these evaluations: performance is examined across demographic groups or testing conditions (gender, age, etc.), to ensure that no unacceptable bias remains [6]. Finally, compliance with legal constraints (such as the GDPR in Europe, which regulates the use of personal data) must be built in from the design phase of the system [6]. That may involve anonymizing data, limiting certain sensitive information, or providing procedures in case an ethical bias is detected.<\/p>

Ultimately, an AI\u2019s performance depends on the quality of the data that trains it. In the forensic field, that means algorithms \u201clearn\u201d from accumulated human expertise. Every algorithmic decision implies the experience of hundreds of experts who provided examples or tuned parameters. It is both a strength – capitalizing on a vast knowledge base – and a responsibility: to carefully select, prepare, and control the data that will feed the artificial intelligence.<\/p>

Technical and operational challenges for integrating AI into forensic science<\/strong><\/strong><\/h2>

Technical and operational challenges for integrating AI into forensic science<\/strong><\/p>

While AI promises substantial gains, its concrete integration in the forensic field faces many challenges. It is not enough to train a model in a laboratory: one must also be able to use it within the constrained framework of a judicial investigation, with all the reliability requirements that entails. Among the main technical and organisational challenges are:<\/p>