Triaging online child abuse material: testing a decision support tool to enhance law enforcement and investigative prioritisation. Issue 4 (2nd March 2021)

• Record Type: Journal Article
• Authors: Mount, David; Mazerolle, Lorraine; Zahnow, Renee; James, Leisa
• Is Part Of: Policing. Volume 44:Issue 4(2021)
• Page Start: 628
• Page End: 642
• Publication Date: 2021-03-02
• Subjects: Decision support -- Triage -- Child abuse material -- Investigative prioritisation; Police -- Periodicals; 363.205
• DOI: 10.1108/PIJPSM-02-2021-0020
• Languages: English
• ISSNs: 1363-951X

Abstract

Abstract : Purpose: Online production and transmission of child abuse material (CAM) is a complex and growing global problem. The exponential increase in the volume of CyberTips of CAM offending is placing information processing and decision-making strains on law enforcement. This paper presents the outcomes of a project that reviewed an existing risk assessment tool and then developed a new tool for CAM triaging and investigative prioritisation. Design/methodology/approach: Using a mixed method approach, the authors first explored the capacity of an existing risk assessment tool for predicting a police action. The authors then used these findings to design and implement a replacement CAM decision support tool. Using a random sample of CyberTip alert cases from 2018, the authors then tested the efficiency of the new tool. Findings: The existing risk assessment tool was not fit for CAM triaging purposes. Just six questions from the old tool were found to be statistically and significantly associated with law enforcement agents achieving a police action. The authors found that an immediate threat of abuse/endangering a child, potential case solvability, CAM image assessment, chat assessment, criticality and some weighting for professional judgement were significant in being associated with a police action. The new decision support tool is more efficient to complete and achieved a 93.6% convergence of risk ratings with the old tool using 2018 case data. Originality/value: This research is unique in its development of an evidence-based decision support tool that enhances the ability of law enforcement agents to objectively and efficiently triage and prioritise increasing numbers of CyberTip alerts.