This in-brief is the third in a three-part series, and it focuses on the value of AI-based text analytics tools to cold case reviews and the identification of physical evidence for additional or advanced testing.
This in-brief is the third in a three-part series that highlights the potential value, approaches, and considerations for digitization of cold case files to facilitate the review process; the authors’ specific focus was on sexual assault and violent crime cold cases, but the technology may also be applied to other types of cold case files. This paper focuses on the value of text analytics and steps that multidisciplinary teams (MDTs) take to adopt [these] technologies. The in-brief’s objectives are to introduce the concept of artificial intelligence (AI)-based text analytics tools to cold-case reviews; identify tools that can be leveraged to quickly draw important insights from information found in cold cases; and illustrate the realities of these emerging tools. Key takeaways suggest that technology implementation may streamline cold case review process; investigators and forensic science service providers (FSSPs) may deploy AI-based text analytics tools to extract information, such as names and types of evidence present, from large text-based sources; commercially available text analytics tools may be adapted to fit law enforcement organizations’ needs, and with external funding or support, some organizations have already implemented text analytics tools; the implementation and maintenance of text analytics tools requires high time, resource, and technical investments; commonly expressed needs for these tools and their high development costs serve as incentives for the development of open-source toolkits.
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