Evidence map›Paper›PMID 39717516›Full record

ArticleForensic science international. Synergy2024

The AI Act in a law enforcement context: The case of automatic speech recognition for transcribing investigative interviews.

Radina Stoykova, Kyle Porter, Thomas Beka

Abstract read
In one paragraph

Article in Forensic science international. Synergy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Radina StoykovaUniversity of Groningen, Broerstraat 5, 9712 CP Grongingen, Netherlands.
Kyle PorterNorwegian University of Science and Technology, Teknologivegen 22, 2815 Gjøvik, Norway.
Thomas BekaNorwegian Police IT-unit, Fridtjof Nansens vei 14, 0031 Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Law enforcement agencies manually transcribe thousands of investigative interviews per year in relation to different crimes. In order to automate and improve efficiency in the transcription of such interviews, applied research explores artificial intelligence models, including Automatic Speech Recognition (ASR) and Natural Language Processing. While AI models can improve efficiency in criminal investigations, their successful implementation requires evaluation of legal and technical risks. This paper explores the legal and technical challenges of applying ASR models to investigative interviews in the context of the European Union Artificial Intelligence Act (AIA). The AIA provisions are discussed in the view of domain specific studies for interviews in the Norwegian police, best practices, and empirical analyses in speech recognition in order to provide law enforcement with a practical code of conduct on the techno-legal requirements for the adoption of such models in their work and potential grey areas for further research.

Indexed as

Artificial IntelligenceArtificial Intelligence Act (AI Act)Automatic speech recognitionGeneral-purpose AI modelsInvestigative interviewsLaw enforcement

Identifiers

PMID39717516
PMCPMC11664072

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.