Integrating AI Systems in Criminal Justice: The Forensic Expert as a Corridor Between Algorithms and Courtroom Evidence

Abstract

Background: Artificial intelligence is transforming forensic fingerprint analysis by introducing probabilistic demographic inference alongside traditional pattern matching. This study explores how AI integration reshapes the role of forensic experts from interpreters of physical traces to epistemic corridors who validate algorithmic outputs and translate them into legally admissible evidence. Methods: A conceptual proof-of-concept exercise compares traditional AFIS-based workflows with AI-enhanced predictive models in a simulated burglary scenario involving partial latent fingermarks. The hypothetical design, which does not rely on empirical validation, illustrates the methodological contrasts between physical and algorithmic inference. Results: The comparison demonstrates how AI-based demographic classification can generate investigative leads when conventional matching fails. It also highlights the evolving responsibilities of forensic experts, who must acquire competencies in statistical validation, bias detection, and explainability while preserving traditional pattern-recognition expertise. Conclusions: AI should augment rather than replace expert judgment. Forensic practitioners must act as critical mediators between computational inference and courtroom testimony, ensuring that algorithmic evidence meets legal standards of transparency, contestability, and scientific rigor. The paper concludes with recommendations for validation protocols, cross-laboratory benchmarking, and structured training curricula to prepare experts for this transformed epistemic landscape.

1. Introduction

Forensic evidence has long served as a cornerstone of criminal adjudication. Among the most venerable and broadly applied methods is fingerprint analysis, which rests on the premise that each individual’s ridge characteristics are unique and persistent. Latent fingermarks are collected at crime scenes and compared against known prints to establish a link between suspect and scene [1]. These traces, often invisible to the naked eye, undergo enhancement and comparison through methodical procedures that have evolved over decades. The reliability of such evidence is not inherent in the physical trace itself but is shaped through expert interpretation and the methodologies used to analyze and present it in legal settings [2].

Forensic fingerprint examiners are certified specialists trained to identify patterns, mark minutiae, and reach conclusions based on established protocols [3]. Their opinions are presented to courts as expert testimony and are often treated with considerable deference. The authority of the forensic expert is grounded in domain-specific training, professional accreditation, and adherence to procedural standards. Their role is not limited to analysis; it also includes translating complex scientific findings into accessible conclusions for legal actors, such as judges, jurors, and attorneys, who lack scientific expertise, and explaining the factors that can influence their conclusions [4].

In the courtroom, the expert’s testimony brings the laboratory findings to the decision-making process. The expert explains how the evidence was collected, how it was analyzed, and on what basis conclusions were drawn. They must also report error-rate studies, validation research, and reliability metrics. This level of disclosure enables defense counsel to challenge the scientific opinion and propose alternative interpretations. Through comprehensive reporting and clear oral testimony, the forensic expert transforms raw physical traces into evidence that meets legal standards of credibility and relevance.

Over time, legal and procedural mechanisms have strengthened demands for transparency and contestability in expert evidence. These demands frame the expert as an intermediary who renders technical findings intelligible and testable in adversarial proceedings [5].

This traditional model is now undergoing a significant transformation. With the rise of artificial intelligence (AI), new predictive tools are being integrated into forensic workflows. In recent years, advances in artificial intelligence and machine learning (ML) have introduced new capabilities to forensic workflows. Supervised learning methods, including classification and regression, are now being applied in forensic contexts. Unsupervised approaches, such as clustering and anomaly detection, also play a growing role. In addition, deep learning architectures have enabled novel applications across multiple stages of forensic analysis. In fingerprint analysis, convolutional neural networks (CNNs) now perform image-based feature extraction, replacing handcrafted filters with automatically learned hierarchical representations [6]. These CNNs can highlight ridge flow and minutiae points with greater robustness to rotation, distortion, and noise than traditional edge-detectors.

The integration of AI into forensic practice raises critical questions about evidence, expertise, and admissibility. Traditional fingerprint analysis yields binary conclusions of inclusion or exclusion based on observable ridge correspondence. AI-based systems, by contrast, generate probabilistic outputs that require interpretation and contextual judgment. The idea of predicting criminality by reading physical features dates back to Cesare Lombroso, whose late nineteenth-century theory asserted that “born criminals” could be identified by congenital stigmata such as sloping foreheads or asymmetrical ears. Lombroso’s approach treated human physiognomy as direct evidence, yet it was criticized early on for its lack of empirical rigor and for conflating correlation with causation. Methodologically, Lombroso’s error is instructive for contemporary debates because it exposes two distinct risks. The first is the overinterpretation risk: treating statistical associations produced by an algorithm as deterministic proof rather than as probabilistic signals that require independent validation. The second is the mechanistic gap: offering predictive claims without grounding them in plausible causal or biomechanical explanations that relate the observable trace to the inferred trait. More recently, Giannini [7] draws a parallel between Lombroso’s discredited physiognomic determinism and contemporary AI systems that predict dangerousness or recidivism using machine learning models. Giannini’s critique shows the need for vigilance when algorithmic tools classify individuals based on inferred attributes rather than direct observation. Just as fingerprint experts base their conclusions on a mechanistic understanding of ridge formation and validated matching procedures, modern AI tools require expert oversight to ensure that statistical inferences do not perpetuate historical inequalities or obscure the limitations of the data.

• This paper examines how the integration of AI into forensic fingerprint analysis transforms the role of the expert and the nature of evidence itself. I focus on the following research questions: How do AI-enhanced workflows differ structurally and epistemically from traditional AFIS-based (Automated Fingerprint Identification System) fingerprint analysis?
• What new responsibilities do forensic experts assume when validating and interpreting algorithmic outputs for courtroom use?
• What standards of transparency, validation, and adversarial readiness must be met for AI-derived evidence to satisfy legal admissibility criteria?

But how will courts adapt traditional admissibility standards, such as Daubert and Frye, that evaluate whether scientific evidence is empirically testable, subject to peer review, and accompanied by a known error rate, to algorithmic evidence? These criteria directly challenge the opacity of machine learning models, which often lack clear pathways for independent replication or systematic audit [8]. What level of transparency in model validation, error-rate disclosure, and audit trails will be required to satisfy both scientific rigor and legal fairness [8]? Frye’s emphasis on general acceptance within the relevant scientific community similarly raises questions about whether AI-based methods have achieved sufficient validation in forensic contexts. Consequently, the integration of AI into evidence law demands not only transparency in model validation and error disclosure but also institutional mechanisms for ongoing audit and reproducibility. Who will oversee AI deployment in forensic practice, and how can oversight bodies combine technical, ethical, and legal expertise effectively [9]? And how must forensic education evolve to include data-science literacy, bias-detection skills, and ethical auditing alongside classical pattern-recognition training? Sallavaci directs analytic attention to the evidentiary form of probabilistic reporting and its procedural consequences. She demonstrates that likelihood-based statements demand precise conditioning, transparent selection of competing propositions, and explicit communication of uncertainty to avoid being misconstrued as categorical proof [10]. Failure to resolve these challenges risks eroding public trust and undermining the presumption of innocence.

In the emerging AI context, the forensic expert’s traditional authority undergoes a fundamental transformation. No longer simply the interpreter of material traces, the expert must now mediate between complex algorithmic outputs and the legal standards that govern admissible evidence. This article examines that transition in depth. It explores how predictive AI tools reshape each stage of the forensic workflow and how experts assume responsibility for validating model performance, translating probabilistic inferences into courtroom-ready language, and integrating synthetic evidence with established legal criteria.

This article explores how the integration of AI tools into forensic fingerprint analysis reshapes the notion of expertise. I will argue that, according to the traditional model, latent fingermarks function as material traces whose value emerges through expert-driven minutiae comparison and manual verification. In contrast, AI systems recast those prints as data sources for probabilistic demographic and behavioral inferences. Such “invisible evidence” [9] generates graded likelihoods rather than binary matches, raising fundamental questions about what counts as valid proof [8].

Are we prepared for the forensic expert’s role to shift from sole interpreter of material traces to an epistemic corridor that mainly validates AI performance, translates probabilistic outputs into courtroom language, and subjects algorithmic inferences to adversarial testing? Can experts acquire the new skills required (e.g., statistical literacy, bias auditing, and model explainability) while retaining their traditional pattern-recognition expertise? Will this hybrid workflow produce leads that are both actionable and legally admissible, uphold scientific rigor, and safeguard the presumption of innocence as forensic practice moves into the AI era?

2. AI Technologies in Forensic Fingerprint Examination

AI and ML present a variety of algorithms that can be applied to forensic tasks. Broadly, supervised learning (classification, regression), unsupervised learning (clustering, anomaly detection), and deep learning (neural networks) are used to process forensic data. In fingerprint analysis, convolutional neural networks (CNNs) have become popular for image-based feature extraction [6]. CNNs automatically learn hierarchical features from pixel data, replacing handcrafted filters. For example, a CNN can learn to highlight ridge patterns and minutiae points better than traditional edge detectors. Deshpande et al. [6] reported Rank-1 identification rates of about 80% on the FVC2004 dataset and 84.5% on the NIST SD27 latent fingerprint set for their CNNAI approach, indicating substantial improvements in latent matching performance under some test conditions. Other fingerprint AI work includes using deep contrastive learning to compare prints and generative models to determine the age of the prints [11]. The researchers used ultrafast DESI-MS to create chemical maps of the fingerprints by measuring lipid profiles and other compounds left behind. This data was then fed into a machine learning model, specifically a variation of the Gradient Boosting Tree ensemble (XGBoost), trained to correlate chemical changes with the known age of the prints. The model achieved an impressive 83.3% accuracy in distinguishing between “fresh” prints (0–4 days old) and “old” prints (10–15 days old).

Beyond fingerprints, AI aids in digital forensics analysis, such as computer logs and mobile device data. For instance, trained machine learning models can identify malicious code, classify files by content, or detect anomalies in user behavior [9]. In forensic imagery, pilot work using large language models (ChatGPT-4, Claude, Gemini) to screen crime scene photos found that the models produced high subjective observation scores as judged by experts, but struggled with complex evidence identification. The study reported average scene observation ratings of 7.8 for homicide images and 7.1 for arson images in a 30-image pilot assessed by 10 experts, underscoring a role as rapid triage rather than final analyst [12]. This suggests a hybrid approach: AI for rapid triage or pattern detection, human experts for final interpretation. In forensic pathology, deep learning is also used to analyze medical scans (CT, MRI) for identification or cause-of-death clues.

Modern algorithms enhance minutia matching by incorporating spatial context. Martins et al. propose validating each minutia via surrounding polygon patterns, making matching robust to translations; for example, their evaluations report equal error rate and false non-match/false match tradeoffs in the low single digits to mid-single digits depending on database and tuning [13]. Other approaches use graph matching, consensus of overlapping patches, or matching 3D fingerprint features (using scan devices). Deep networks can also learn latent fingerprint enhancement, removing noise and inferring missing ridges [6]. There are also efforts to detect fingerprint spoofs or forgeries using CNN detectors and multimodal biometrics for liveness detection. Recent work by Spanier et al. [14] builds on these techniques to achieve advanced gender classification from partial prints, using data-centric AI models trained across multiple fingerprint databases to demonstrate both high accuracy (70–95% across different datasets) and cross-population robustness.

Internationally, forensic institutes are integrating AI into standards. In the United States, the National Institute of Standards and Technology (NIST) has a central role in developing standards for forensic algorithms. It maintains benchmark datasets that allow for the systematic testing of latent and tenprint fingerprint algorithms. These datasets are used to assess accuracy and reproducibility in automated matching systems. In recent years, NIST has also examined how AI-based systems may introduce demographic bias and how such bias can affect forensic outcomes. This shift reflects an understanding that forensic algorithms must be evaluated not only for technical performance but also for fairness and transparency. NIST’s approach provides an example of how national forensic institutions can incorporate AI tools while maintaining scientific and ethical integrity [15]. In Europe, proposals (and in Israel, privacy authorities) are considering regulations for biometric data (e.g., how law enforcement uses face/fingerprint recognition). As noted by experts, these evolving AI tools require rigorous evaluation against quality metrics (accuracy, error rates) to be court-admissible [16].

AI in forensic prediction spans from automating routine tasks (matching prints, reading DNA sequencers) to advanced analysis (pattern discovery, simulation). Machine learning, particularly deep learning, has become a powerful new “forensic expert” of its own, capable of learning from large datasets and uncovering patterns or connections that human analysts might overlook. However, unlike a human expert who offers reasoned, interpretable testimony, these models often produce results that lack intuitive or transparent explanations. This shift introduces a fundamental epistemic transformation in forensic science. The forensic expert is no longer the exclusive source of interpretive authority, but functions as a mediator who validates and translates algorithmic outputs into scientific and legal frameworks.

In this capacity, expertise is exercised through the evaluation of algorithmic reliability, transparency, and methodological soundness. The emergence of probabilistic, machine-generated outputs requires the expert to ensure that these results meet evidentiary standards of accuracy and credibility. This evolving role reflects the broader adaptation of forensic practice to a data-driven environment in which human judgment remains central to the translation of computational inferences into legally meaningful conclusions.

3. Methodology

This study applies a comparative process-tracing approach to evaluate two distinct workflows in forensic fingerprint analysis: (1) the conventional physical-evidence model based on AFIS and expert validation, and (2) an AI-enhanced predictive model that applies machine learning to infer demographic features from partial, low-quality latent fingermarks. The methodological objective is to simulate, through a conceptual exercise, how each workflow would function when presented with the same forensic input under realistic investigative conditions. This is not an empirical study with real-world data of validated AI systems, but a structured thought experiment designed to explore the implications of integrating AI into forensic practice. All references to confidence scores, demographic classifications, fingerprint parameters, and candidate yields in this section are illustrative.

3.1. Sample and Scenario Design

A theoretical case scenario was constructed by the author as a conceptual, literature-ground exemplar, in which a partial latent fingermark was recovered from a residential burglary scene. The print was assumed to contain approximately 25% ridge coverage, a level at which prior research has shown that sufficient valid features can still be extracted for matching, despite the partial or fragmented nature typical of real-world fingermark evidence [17]. The same image serves as input for both workflows, allowing conceptual comparison of operational effectiveness. Both analyses were performed by the same certified forensic fingerprint expert with fifteen years of casework experience. This ensures consistency in interpretation and highlights how expertise interacts differently with manual and AI-based processes.

3.2. Traditional Workflow

The traditional forensic process proceeds as follows:

Image Acquisition: The print is digitally captured at 1000 dpi and enhanced using standard forensic image processing techniques (contrast adjustment, noise reduction).

Minutiae Extraction: The print is imported into a standard AFIS platform, where minutiae points (e.g., ridge endings, bifurcations), core, and delta are algorithmically extracted.

Candidate Matching: The AFIS system compares the encoded print against a national reference database. A ranked list of candidates is generated based on similarity scores.

Expert Analysis: This stage represents the traditional interpretive authority of the human examiner, who determines whether sufficient correspondence exists for an identification or exclusion. A certified fingerprint expert compares the print, side by side, with the top-ranked AFIS candidates. The process includes visual inspection, ridge pattern comparison, and documentation of correspondences.

For interpretive consistency in this conceptual exercise, the procedural steps above were instantiated by a certified latent print examiner who is the author of the paper and who draws on professional casework experience.

3.3. AI-Based Predictive Workflow

The AI-enhanced model consists of five stages:

Image Enhancement: The same print is processed using a residual convolutional neural network (ResNet-based architecture) for latent fingermarks enhancement, following the methodology of [6].

Demographic Feature Extraction: The hypothetical enhanced image is passed through a CNN ensemble trained on multi-database fingerprint datasets to predict demographic attributes, including biological sex (binary classification), estimated height range (tall/short), hand laterality, finger position, and likely ethnic group (broad classification). These predictions are hypothetical and intended to represent the type of outputs that an AI model could generate under current research trends [14].

Candidate Filtering: The inferred profile is used to query a facial image repository containing 10,000 mugshots. Face embeddings are compared using cosine similarity, reducing the candidate pool to the top ten matches.

Model Explainability: Saliency maps (e.g., Grad-CAM) are generated to visualize the regions of the fingerprint image that influenced each demographic classification. This method employs gradient-based class activation mapping to produce heatmaps highlighting the precise ridge features driving each inference, thereby enabling expert validation of the algorithm’s focus. The purpose is to illustrate how explainability tools can support expert oversight, not to validate specific network performance.

Expert Audit: The same forensic analyst reviews all AI outputs, verifies confidence intervals, and determines whether the demographic profile is consistent with crime scene context and investigative needs. The same expert who conducted the traditional analysis performed this conceptual review to ensure consistency across both workflows. This stage emphasizes the interpretive role of the expert as an epistemic corridor, validating AI outputs and contextualizing them within forensic reasoning.

4. Results and Discussion

This section presents the outcomes of the comparative scenarios and workflows described in Section 3. A broader discussion on the implications of predictive AI in forensic practice, drawing on recent scholarship in criminology, ethics, and AI-law interfaces, will be followed.

4.1. Results

The demographic predictions reported in the AI workflow are the direct confidence estimates produced by the fitted classifiers. These outputs are model-reported probabilities and do not substitute for empirically derived measures of diagnostic performance such as sensitivity, specificity, positive predictive value, or negative predictive value. This article adopts a comparative, process-tracing design to explore epistemic and procedural implications of integrating such outputs into casework. Comprehensive sensitivity/specificity analyses on partial fingerprints were outside the present study’s scope; however, I outline a validation agenda in the Discussion and acknowledge the need for such empirical work before operational deployment.

In the constructed burglary scenario, the traditional AFIS-based workflow was assumed to have failed to produce an identification. Despite manual minutiae annotation and exhaustive expert comparison, no candidate achieved the required concordance threshold, and the examiner recorded a “no identification” result. This could happen since the human expert missed the corresponding records of the perpetrator, or the records are not included in the database.

Conversely, the hypothetical AI-enhanced predictive workflow was modeled as yielding a demographic profile: male; height 175–185 cm; right-hand middle finger; likely of North African/European ancestry. These values represent the model’s internal confidence estimates and were used here for illustrative and comparative purposes only. Cross-referencing these attributes, derived from a thought experiment, against a regional facial image repository produced eight plausible suspects. Upon expert audit of Grad-CAM saliency maps and confidence intervals, one individual was highlighted for follow-up. Subsequent traditional investigation was simulated in the scenario, which led to the suspect’s arrest within 48 h of initial analysis.

These findings illustrate that, in cases where the classical AFIS model yields no match, AI-based demographic inference can generate actionable leads, effectively expanding the boundaries of forensic utility beyond direct pattern matching.

4.2. Discussion

4.2.1. Transition from Physical to AI-Based Forensic Evidence

The shift from traditional, physical-evidence analysis toward AI-driven inference represents a fundamental transformation in forensic epistemology. In classical fingerprint examination, the latent fingermark is treated as a material trace, whose evidentiary value depends on expert-driven minutiae comparison and established comparison protocols [1]. This approach grounds forensic conclusions in visually verifiable and replicable features. In contrast, AI-based workflows reframe the same trace not merely as a physical artifact but as a digital input for probabilistic demographic and behavioral inference [6,18]. The comparative scenario reported in Section 3 illustrates a concrete pathway by which AI-derived inferences enter casework. Building on this transformation, Tynan argues that the integration of predictive tools extends the reach of forensic science into domains previously reliant on human intuition, enabling novel lead generation in cases where traditional matching fails [8].

Yet this expanded reach comes at a cost. The ontological shift from discrete physical comparisons to algorithmically inferred probabilities challenges conventional definitions of what counts as evidence. While traditional pattern-matching yields binary conclusions of inclusion or exclusion, AI predictions offer graded likelihoods that lack visual or material anchoring. These must be interpreted with caution, especially in legal contexts where clarity, transparency, and contestability are paramount [8]. Extending this line of critique, Klasén et al. describe such AI-derived evidence as “invisible” because it is produced through algorithmic pattern recognition rather than through observable, physical characteristics [9]. The forensic community must therefore reconcile two modes of knowledge: replication of observable patterns and model-based probabilistic inference. While digital forensics has long operated with intangible data like log files or metadata, AI-driven inference intensifies this trend by extracting latent human attributes (e.g., gender, height, or ancestry) from degraded or partial fingerprint images. This evolution compels forensic science to reconcile between two epistemic modes: the material certainties of traditional pattern matching and the probabilistic, often opaque reasoning embedded in machine learning models.

4.2.2. The Evolving Role of the Forensic Expert

The comparative scenario (Section 3) provides a concrete setting to examine the evolving expert’s role. Forensic experts have traditionally served as the primary interpreters of evidentiary findings in criminal investigations and courtroom proceedings. The arrival of AI requires a reconceptualization of expertise. In the presented scenario, AI functioned as an investigative aid, while experts combined traditional pattern-recognition skills with competencies in model validation and explainability. Recent studies highlight that current AI systems should complement, not replace, human expertise. For instance, Farber [12] demonstrated that AI can facilitate rapid initial screening of crime scene images, highlighting relevant areas for further analysis. However, final conclusions still necessitate critical human review to ensure reliability and admissibility; “While these tools can enhance the capabilities of resource-constrained agencies, they must be implemented with appropriate safeguards” [12].

The evolving domain of forensic science requires practitioners, particularly expert witnesses, to acquire new competencies and novel forms of expertise. A thorough understanding of how AI algorithms function is essential, along with the ability to interpret statistical outputs such as confidence levels and identify scenarios prone to false positives or negatives. Experts are expected to assess algorithmic performance against established benchmarks and be equipped to critically evaluate and articulate AI-generated findings within legal settings.

For example, in the scenario, the AI relied on ridge curvature patterns rather than traditional minutiae. Without understanding this novel marker, an examiner might dismiss a correct match or fail to challenge a flawed one. Thus, comparing AI saliency to forensic feature validity is essential before the AI output is treated as an investigatory lead. Jurors, too, require clear explanations of why the algorithm focused on certain features.

In this role, the expert serves as an epistemic corridor, acting as a vital channel that conveys knowledge from complex computational systems into the legal domain. The expert validates the performance of AI tools, translates probabilistic and algorithmic outputs into accessible and comprehensible testimony, and finally, bridges the gap between laboratory analysis and courtroom evidence (Figure 1). This stewardship safeguards the integrity of forensic practice, ensuring that AI technologies augment rather than undermine the reliability and legitimacy of expert findings.

The designation “epistemic corridor” highlights the expert’s role as a narrowly defined and controlled pathway. This pathway conveys reliable and substantiated knowledge from the domain of technical complexity to the judicial context. This corridor is necessary because the probabilistic nature and inherent uncertainty of AI outputs require interpretation and contextualization before they can serve as trustworthy evidence. The expert thus mediates between the often-opaque algorithmic processes and the demands of legal reasoning, ensuring that knowledge entering the courtroom is both intelligible and epistemically sound.

In essence, the epistemic corridor metaphor highlights the pivotal function of the forensic expert as an intermediary who preserves the quality and credibility of knowledge in the translation from technological outputs to legal decision-making. This role is crucial for integrating advanced AI tools into forensic workflows without compromising evidentiary standards or the pursuit of justice.

Within their role as an epistemic corridor, the forensic experts assume the critical responsibility of technical validation and audit, ensuring that algorithmic tools meet the evidentiary thresholds required for legal admissibility. This task begins with a forensic-level evaluation of the AI system’s performance metrics, including error rates, confidence intervals, false positive and false negative ratios, and the presence of demographic or contextual biases. Such scrutiny is not merely a technical exercise but a fundamental epistemic function that safeguards the transition from computational inference to courtroom legitimacy.

To fulfill this role, the expert is expected to engage with validation studies conducted under conditions approximating casework reality, applying established forensic standards to assess whether the AI system’s behavior remains stable across different substrates, image qualities, or population groups. For example, in latent fingerprint analysis, this may include assessing the algorithm’s performance on low-ridge-density impressions or prints recovered from textured surfaces. The expert must also verify that the system adheres to traceability principles; that each step from image input to classification output can be reconstructed and audited.

This aspect of the expert’s role resonates with broader concerns in legal scholarship about the admissibility of algorithmically derived evidence. As Brayne and Christin [19] and Kawamleh [20] have noted, courts often express hesitation when confronted with AI systems whose internal logic is opaque or inaccessible to adversarial testing. The expert must therefore bridge the epistemic gap between the system’s internal processes and the legal demand for transparency. This includes not only verifying that audit trails exist, but that they are interpretable and contestable within an adversarial legal forum.

By performing this level of technical validation, the forensic expert ensures that AI systems do not become unaccountable black boxes, but evidence-producing instruments embedded within scientifically grounded and legally coherent practices. Therefore, the expert’s role does not fade in the face of AI, but becomes even more indispensable, serving as both a gatekeeper of forensic integrity and an epistemic corridor that guides algorithmic predictions through the rigorous scrutiny required for legal legitimacy.

• Scientific Translation

One of the most critical functions of the forensic expert is to transfer the conclusion and opinion based on physical evidence to the court [21]. This becomes more challenging when talking about an AI-enhanced workflow, while the forensic expert is expected to supply the translation of algorithmic inferences into legally meaningful and epistemically credible terms. Unlike traditional forensic evidence, for instance, a visible fingermark match, which can be illustrated through annotated ridge overlays, the outputs of AI systems are often multidimensional and opaque to laypersons, including judges and jurors.

Edmond [22] emphasizes that scientific evidence must be presented in a manner that is both clear and comprehensible within the adversarial structure of legal proceedings. This requirement takes on particular urgency in the context of AI-generated forensic outputs, which risk being perceived as inaccessible or opaque “black boxes”. Therefore, forensic experts should not treat algorithmic conclusions as unchallengeable truths. Rather, they must actively render these outputs transparent and subject to scrutiny. This involves more than merely reporting results; it requires explaining the methods used to derive them, disclosing error rates and validation data, and articulating the limitations of the AI system in a way that enables meaningful cross-examination by opposing counsel. Therefore, experts help ensure that algorithmic evidence can be properly weighed and contested in court, preserving the foundational principle of adversarial testing that Edmond argues has been historically neglected in forensic science.

Jasanoff [5] describes this translation task as a key duty of the expert. She calls the expert a “boundary actor”. This actor bridges the differing standards and expectations of the scientific and legal communities.

The experts thus serve as a narrative and epistemic interpreter. They do more than relay technical results. They shape these results so the probabilistic nature remains intact and is suitable for legal discussion. Without this process, the courts risk misinterpreting or placing too much trust in the apparent authority of the algorithm. This danger increases when the reasoning of the algorithm is opaque. Translation is therefore not just about making evidence accessible; it is essential for preserving the integrity of inference in a legal context.

2.

Adversarial Readiness

Within the adversarial structure of the legal system, the forensic expert functioning as an epistemic corridor must ensure that algorithmic outputs are contestable and auditable. This involves providing the defense with comprehensive information about the algorithm’s development and application. Such disclosure is essential for enabling informed cross-examination and fulfilling the principle of procedural fairness.

This approach parallels the historical development of forensic fingerprint testimony. As noted by Edmond [22], legal contestation over time required fingerprint experts to clarify their matching methodologies, report known error rates, and justify their conclusions in court under adversarial examination. In a comparable manner, forensic tools driven by AI should not be insulated from such scrutiny by invoking algorithmic complexity or proprietary constraints. By facilitating this level of scrutiny, the forensic expert bridges the scientific and legal domains, ensuring that algorithmic findings meet the evidentiary standards. In this boundary-spanning role, the expert not only verifies the scientific integrity of AI systems but also safeguards the justice process by supporting informed, fair decision-making based on critically evaluated evidence.

As AI handles more analytic tasks, the forensic expert’s focus shifts away from direct examination of physical traces. The expert now validates and interprets algorithmic outputs. In this role, they become an epistemic corridor, guiding AI-generated findings from the laboratory into courtroom evidence. Ryan [23] critiques narrow “human-centered AI” frameworks that treat algorithms as passive tools under full human control. He argues instead for attention to the socio-technical networks in which expertise is co-produced, highlighting how power and technology jointly shape analytic outcomes.

Under Daubert-style review, courts will require empirical testing, known error rates, and peer review. In this scenario, AI systems that cannot meet these criteria risk exclusion or severe limitation in evidentiary use. Experts must therefore link model validation data to case conditions to show relevance. This careful audit is essential for ensuring both analytical accuracy and legal admissibility [8,24].

3.

Evidence validation

Forensic experts remain central to this new workflow. As epistemic corridors, they translate complex inferences into courtroom-ready testimony. They validate AI findings against known scientific standards and ensure that algorithmic recommendations align with investigative goals. Training programs must therefore equip experts with both data science and AI ethics. At the same time, they must reinforce traditional pattern recognition methods. This blended approach secures credible, expert-guided evidence in the AI era.

Yet as experts take on this expanded role, the nature of the evidence they engage with is also transforming. The emergence of AI-derived evidence forces a rethink of the forensic expert’s role. Sallavaci [10] highlights how probabilistic reporting challenges the presumption of innocence. Physical evidence was once concrete and material. AI outputs are synthetic and probabilistic.

In this new point of view, the expert becomes an epistemic corridor. The expert must translate AI inferences into legal terms and validate model performance. The expert should explain uncertainty and must guide courts through complex algorithmic logic. This critical mediation can determine whether AI evidence is trusted or rejected.

But should we embrace this transformation? Are we prepared to grant AI-based inference the status of admissible proof? What standards must we adopt to ensure fairness and transparency? How do we train experts to audit bias and communicate probabilistic conclusions clearly? These questions must be answered before synthetic evidence can take its place in the judicial systems.

To fulfill this corridor role, forensic experts validate AI models and translate probabilistic findings into terms judges and jurors can use. They also supply defense counsel with the information needed to challenge algorithmic outputs. These practices transform invisible computational inferences into legally credible evidence. Therefore, experts maintain scientific rigor and uphold legal fairness, safeguarding the integrity of the criminal justice system amid rapid technological change.

4.2.3. Epistemic and Ethical Considerations

AI-driven inference introduces probabilistic reasoning into forensic workflows, challenging long-standing notions of certainty and objectivity. Joseph [25] cautions that when training data mirror historical inequities, algorithmic predictions can perpetuate existing biases in investigative leads and judicial outcomes. In our comparative scenario, demographic classifiers trained on diverse fingerprint datasets still risk over- or under-representing specific populations. However, algorithmic inference raises ethical concerns that extend beyond fingerprint analysis. Bias risk is systemic across forensic domains. Importantly, similar evaluator-related biases appear in other forensic specialties. For example, forensic psychiatry demonstrates how diagnostic and gender biases can shape assessments of responsibility and dangerousness [26]. Acknowledging these parallels strengthens the claim that AI integration interacts with pre-existing cognitive and structural vulnerabilities rather than creating them de novo. Thus, bias audits and structural safeguards should be applied across forensic disciplines, not only to biometric applications.

Hefetz [24] stresses that AI outputs should be treated as investigative tools, not as conclusive proof. This distinction is central to the discussion in this study, which explores the shift from physical to synthetic forms of forensic evidence. Traditional evidence relies on material traces such as latent fingermarks. These are concrete and observable. In contrast, AI systems generate inferences based on probabilities. These outputs do not reflect direct observations. They are synthetic constructs that must be interpreted with care.

The concept of boundary objects helps explain how certain forms of evidence can operate across both scientific and legal domains [5]. Expert reports have long served this dual role by meeting the methodological standards of forensic science while remaining intelligible and admissible in court. Algorithmic outputs now assume a similar position. These AI-generated inferences, often described as “invisible evidence”, lack a material form but claim evidentiary relevance through patterns extracted from complex data [9]

Predictive crime algorithms are often seen as tools for prioritizing investigations rather than as standalone proof in criminal trials [19]. This reflects a co-production process in which legal actors and technologists jointly establish the criteria under which algorithmic inferences may achieve evidentiary status.

In the scenario presented here, the AI-generated suspect profile prompted further investigation. The inquiry moved forward, and a suspect had been arrested. Does this reflect a stance of misuse of forensic evidence? It seems that AI may be used to support the work of human experts, not to replace their judgment or responsibility.

This approach reinforces the need for expert oversight. It also highlights the importance of transparency when synthetic evidence enters the legal process. AI tools can contribute meaningfully to investigations, but their role must remain supportive. They complement human interpretation rather than replace it.

4.2.4. Implications for Policy and Practice

The integration of AI into forensic science requires comprehensive policy frameworks and institutional safeguards. Tynan advocates for standardized validation protocols to ensure algorithmic evidence meets Daubert-like admissibility criteria [8].

Although Klasén only emphasizes the need for cross-sector collaboration in digital forensics, in this study, I recommend the establishment of transdisciplinary oversight bodies combining legal scholars, practitioners, ethicists, and technologists [9].

In practice, forensic laboratories should test AI tools under supervision and should publish performance metrics. They should also promote public discussion about privacy and fairness, because acceptance of algorithms in policing and courts depends on both technical reliability and social trust [19]. When experts validate the tools and report results clearly, the forensic field can build confidence in AI-supported methods. This helps maintain core values such as accuracy, responsibility, and fairness, along with legitimacy and due process in the justice system.

5. Conclusions

AI-driven systems are reshaping forensic fingerprint analysis by combining biometric data with machine-learning models.

This work offers a conceptual reflection on the evolving role of AI in forensic fingerprint analysis, supported by illustrative scenarios. The shift from traditional reliance on physical evidence to probabilistic, algorithm-based inferences positions the forensic expert as a critical intermediary between material traces and digital outputs.

Given the technical and ethical complexities, I recommend the development of standardized validation protocols to systematically assess AI system accuracy and reliability before deployment. Cross-laboratory benchmarking initiatives can facilitate performance comparisons, identification of biases, and harmonization of standards across forensic institutions.

Furthermore, practitioners should receive training in data science literacy for the understanding of algorithm fundamentals and in bias detection techniques to mitigate unfair outcomes. Incorporating adversarial testing, which involves evaluating AI robustness against deliberately manipulated inputs, can enhance system reliability and accountability.

Establishing transparent validation procedures and fostering collaboration among stakeholders are essential steps toward the responsible and trustworthy integration of AI in forensic science.