Point 6(c) of Annex III classifies as high risk AI systems intended to be used by or on behalf of law enforcement authorities, or by Union institutions, bodies, offices or agencies in support of law enforcement authorities, to evaluate the reliability of evidence in the course of the investigation or prosecution of criminal offences. The provision refers to ‘evaluation of reliability of evidence’ and as such focuses only on this specific task and only in the course of an open criminal investigation or prosecution.
In the context of law enforcement activities, evaluating the reliability of evidence means assessing how trustworthy, accurate, and credible the evidence is in the case it is used in investigations, arrests, or prosecutions. This encompasses, in particular, the court-proof verification of the authenticity of evidence prior to conducting in-depth investigations or making arrests or other prosecussion action. The core function of AI systems covered by this use case is the evaluation of evidentiary reliability. This may include, for instance:
- authenticity verification;
- data integrity checks;
- source reliability assessment;
- evaluation whether the evidence aligns with other available information or is internally consistent.
This does not include AI systems that are intended to be used for working with the information collected, but that are not intended to be used for the evaluation of reliability of evidence. Examples of such tasks that are not falling under the scope of ‘evaluation of reliability of evidence’ (and thus are outside of point 6 AI Act) , include collecting, arranging and structuring of information, data extraction and linking, document clustering, timeline reconstruction, visualisation, indexing of documents, duplicate detection, search in its various forms (including, for example, keyword-based queries, similarity/fuzzy, knowledge‑graph search, and multimodal and semantic retrieval across text, audio, image or video , translation and transcription, as well as data structuring and standardisation.
a) Practical examples of AI systems falling within the use case of point 6(c)
Authenticity verification
- AI-enabled digital forensics solutions for image, audio and video analysis intended to beused by law enforcement authorities to detect whether digital images, audio recordings or videos have been altered or manipulated (e.g. to confirm the authenticity of CCTV footage or smartphone recordings that were presented as evidence; to verify if collected pictures are not deepfakes). Data integrity checks and source reliability assessment
- AI systems intended to be used by law enforcement authorities to determine and evaluate alterations of documents , or forged signatures.
- AI systems intended to be used for evaluating the authenticity of digital evidence retrieved from mobile or other devices , including by analysing digital traces (e.g. metadata such as geolocation records, file creation and modification histories) and by flagging elements requiring closer human examination.
- AI systems intended to be used to assist competent law enforcement authorities in assessing the reliability of evidence , including from human sources , such as the credibility of informants or witnesses, by analysing factors such as the internal consistency of statements, their coherence with verified facts, and the accuracy of information provided by the same source in past cases.
b) Practical examples of AI systems falling outside the use case of point 6(c)
Image enhancement and data recovery
- AI systems intended to be used for forensic reconstruction, image enhancement, or data recovery from devices. Such systems do not evaluate the reliability of evidence, but assist human experts by processing, restoring, or extracting data that can then be examined and relied upon. AI systems reconstructing crime scenes
- AI systems intended to be used to map or reconstruct crime scenes or to provide visualisations or models based on data collected (photos, measurements, etc.). Such systems help investigators better understand the spatial relationships and sequence of events at a crime scene without assessing evidence reliability. Pattern detection
- AI systems intended to be used to detect patterns in investigations and notify law enforcement authorities thereof (e.g. identifying similar break-in methods in recent thefts or notify of similar victim profile in recent homicides). Such systems do not evaluate the reliability of evidence , but find correlations or patterns that might otherwise go unnoticed and help law enforcement focus their investigations or spot potential links between cases. Content flagging and searches
- AI system s intended to be used to detect child sexual abuse material online , without evaluating the reliability of it as evidence . T he primary function of such a system is to identify, flag and filter content that potentially violates the law.
- AI-enabled solutions intended to be used by law enforcement authorities for screening child sexual abuse material online and the classifying the reof in the following groups for prioritisation: (i) new material; (ii) material already registered in the database; and (iii) deepfake pictures. Such systems do not evaluate the reliability of evidence, but are designed to support investigative workflows by filtering or preselecting large volumes of incoming material. The application of Article 6(3) of the AI Act i n this use-case is without prejudice to the potential qualification of the AI system at issue as high-risk system under other use cases listed in Annex III of the AI Act (e.g. point 6(a) of Annex III).
- AI system s intended to be used by law enforcement authorities to analyse online advertisements in the sex industry and to flag those most likely to be linked to human trafficking. Such systems do not evaluate the reliability of evidence , but a re designed to identify, flag and filter content that potentially violates the law.
- AI system s intended to be used to match the ballistic markings on a bullet to markings registered in a reference database for the purposes ofidentifying whether a particular weapon was used for committing a crime. Such system s do not evaluate the reliability of evidence , but constitute a means of file handling or of searching a database by evaluating the similarities between evidence material and a reference database.
c) Practical examples of AI systems falling within the use case but exempted by the filter mechanism in Article 6(3) AI Act
- AI systems that are used in the course of evaluation of evidencebut only classify documents, pictures, videos or other material selected to be analysed for authenticity into categories and thus structure the evaluation of evidence where that categorisation does not impact the assessment of the reliability of those documents as evidence. Such systems fall under the exception for AI systems intended to perform narrow procedural tasks in Article 6(3)(a) AI Act.
- An AI system that is used in the course of evaluation of evidence and isindexing and tagging financial documents collected to be evaluated for reliability of evidence (e.g. to be checked with bank account flows; or to be checked for existence of names or objects). Such system fall under the exceptions for AI systems intended to perform narrow procedural or preparatory tasks in Article 6(3)(a) and (d) AI Act.