Stoykova Radina, Porter Kyle, Beka Thomas
University of Groningen, Broerstraat 5, 9712 CP Grongingen, Netherlands.
Norwegian University of Science and Technology, Teknologivegen 22, 2815 Gjøvik, Norway.
Forensic Sci Int Synerg. 2024 Dec 5;9:100563. doi: 10.1016/j.fsisyn.2024.100563. eCollection 2024.
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.
执法机构每年要人工转录数千份与不同犯罪相关的调查访谈记录。为了实现此类访谈转录的自动化并提高效率,应用研究探索了人工智能模型,包括自动语音识别(ASR)和自然语言处理。虽然人工智能模型可以提高刑事调查的效率,但其成功实施需要评估法律和技术风险。本文探讨了在欧盟人工智能法案(AIA)背景下将ASR模型应用于调查访谈的法律和技术挑战。从挪威警方访谈的特定领域研究、最佳实践以及语音识别的实证分析等角度讨论了AIA的条款,以便为执法部门提供一份关于在其工作中采用此类模型的技术法律要求以及进一步研究潜在灰色区域的实用行为准则。