AI and evidential disclosure 1173/2025

FOI Reference: 1173/2025

Request

I am investigating how AI-assisted evidence analysis is documented and reported within existing Chain of Custody (CoC) and Audit Trail (AT) structures.

For the sake of clarity, I have included a glossary at the end of this document. CoC is used here as an umbrella term encompassing related concepts such as continuity, chain of evidence, and provenance; while AT refers to any documentation system that captures and records the forensic processing of evidence.

1. How is evidential disclosure managed in criminal proceedings where AI systems are used in processing and analysing forensic audio, textual data, or other forms of evidence?
2. Please specify the nature of materials disclosed to defence teams (e.g. final transcript only or detailed version history; original audio; AI model metadata etc?
3. Are the documentations of CoC and/or AT disclosed to the defence team? If yes, in what format is this made available?
4. Is there a standard procedure or checklist governing this? If so, please provide a copy or reference it.

Glossary

• Artificial Intelligence (AI): This refers to a machine that learns, generalises, or infers meaning from input, thereby reproducing or surpassing human performance. The term AI can also be used loosely to describe a machine's ability to perform repetitive tasks without guidance.
• Audit Trail (AT): Refers to the documentation processes that capture, record, and report the sequence of actions applied to digital evidence. An AT may include details such as model configurations, parameters used, error rates, human interventions, and system-generated logs. It functions as a transparency mechanism, enabling the reconstruction and evaluation of how evidence has been processed, analysed, and interpreted.
• Automatic Speech Recognition (ASR): A subfield of AI that processes spoken human language (audio) and converts it into written text, useful for tasks like transcription and speaker diarisation.
• Chain of Custody (CoC): This is the record of how evidence is handled from the point of collection through processing to presentation in court. This may include the identity of the person handling the evidence, date/time of transfer, storage details, and digital identifiers. Alternative terms include 'continuity', 'chain of evidence' and 'provenance'.
• Forensic Audio evidence: This refers to recorded sound collected, preserved, and analysed for legal or investigative purposes. This often includes spoken language from sources including body-worn cameras, dashcams, police radio communications, and interview or interrogation recordings.
• Forensic Textual evidence: This refers to written or encoded content, stored or transmitted in digital form, collected, preserved, and analysed for legal or investigative purposes. It includes unstructured human language (e.g., transcripts, emails, chat logs, social media posts), semi-structured data (e.g., system or network logs, ASR outputs), and structured data (e.g., metadata, timestamps).
• Large Language Models (LLMs): A type of Natural Language Processing (NLP) system trained on vast amounts of text to perform sophisticated language tasks like Is there a standard procedure or checklist governing this? If so, please provide a copy or reference it.

Response

 I can confirm that Dyfed-Powys Police does not hold the information requested, this is due to the fact that we do not utilise any AI tools for disclosure/file build purposes.

It should be noted that as a result of the systems adopted by Dyfed-Powys Police in relation to the recording of such information that the information released may or may not be accurate.

(This is a response under the Freedom of Information Act 2000 and disclosed on 17/12/2025)