{"__content__"=>"\n Objectives\n \n \n Methods\n \n \n Results\n \n \n Conclusion\n \n ", "p"=>[{"__content__"=>"The current study measures whether AI-generated transcripts of body-worn camera footage accurately capture who is speaking and what is said, compared to human-edited transcripts."}, {"__content__"=>"We analyzed AI and human-edited transcripts from 176 body-worn camera videos (73 incidents, approximately 23 h of footage) from a large U.S. police department. We used multiple text-similarity metrics to compare speaker extraction, speaker matching, and overall document similarity."}, {"__content__"=>"AI-generated transcripts captured much of the same lexical content as human-edited transcripts but performed noticeably worse on identifying and attributing speakers, especially in complex, multi-speaker encounters."}, {"__content__"=>"AI transcription appears suitable for large-scale content search and descriptive text analysis but should not replace human transcription for attribution-sensitive uses. The findings underscore the value of hybrid human-AI workflows and the need for clear policies distinguishing low-stakes from high-stakes uses of AI-generated transcripts in policing."}]}