Artificial Intelligence and Digital Evidence in Criminal Process

Spanish criminal law faces a paradigmatic change: artificial intelligence has entered forensic evidence, both on the prosecution's side (predictive models, facial recognition, algorithmic analysis of evidence) and on the criminals' side (deepfakes, synthetic voice, AI forgeries). Modern criminal defense requires contradictory technical capacity over each piece of evidence of algorithmic origin: it is no longer enough to argue the law; one must be able to discuss the model, the data and the method. This page is the gateway to the area; each vector also has its own detailed analysis.

The New Evidentiary Standard in the AI Era

The EU AI Regulation (2024/1689) has established risk categories and transparency obligations affecting the forensic use of models. When an investigation or accusation relies on AI, the defense can demand four guarantees: (1) traceability of the model used —which algorithm, which version, which training data—; (2) a bias audit and the error rate applicable to the specific profile; (3) reproducibility of the result by an independent expert with documented methodology; and (4) significant human supervision, not merely formal. The absence of any of these elements compromises the admissibility of the evidence and opens the way to its exclusion under the clause of Article 11.1 LOPJ. Unlike traditional evidence, algorithmic evidence is not validated by the appearance of reliability, but by its capacity to be subjected to effective contradiction.

Deepfakes: Detection and Challenge

The first major front is synthetic audiovisual content. A deepfake submitted as prosecution evidence may have been fabricated or altered through generative adversarial networks (GAN) or diffusion models, and its challenge requires specific forensic expert evidence: frequency analysis, search for generation artifacts, biometric inconsistencies and verification of metadata and of the watermarks that Regulation 2024/1689 imposes on synthetic content. On the opposite side, when the deepfake is the instrument of the offense (sextortion, slander, impersonation), we represent the victim by exercising the private prosecution. We develop this vector on the page on challenging deepfakes as criminal evidence.

Voice Impersonation and Vishing

AI voice cloning has made accessible attacks that previously required sophisticated means: with a sample of a few seconds, a synthetic voice is generated capable of sustaining a credible call. The result is CEO fraud, family impersonation in fake emergencies and telephone banking fraud. The defense —both of the accused and of the affected company or individual— relies on contradictory acoustic expert evidence and on the analysis of the attack vector and the bank's diligence. We address it at length in AI vishing and voice impersonation.

Audit of Algorithmic Models

When the prosecution relies on a model —facial recognition, automated analysis of image, audio or text— the evidence is only admissible if it is traceable, reproducible and supervised. The defense can demand the full documentation of the model and commission an independent algorithmic expert report that reproduces the result, audits the dataset and contrasts the error metrics. The provider's trade secret is not an absolute shield against the right of defense when the evidence is decisive. This line is developed in algorithmic expert evidence.

Bias in Predictive Investigation

Finally, when the very decision to investigate stems from a predictive model —territorial predictive policing, hotspots or individual recidivism profiling— the defense can question the very origin of the suspicion. Models trained on historical complaint data inherit and amplify prior biases, which affects the reasonable indication, the proportionality of the measures and the presumption of innocence. We analyze the procedural and constitutional exclusion strategies in bias in predictive models.