We are producing a wide range of material including academic publications, technical notes, reports, white-papers, videos, and code.
Academic publications
Effective, Explainable and Ethical: AI for Law Enforcement and Community Safety

Wilson, C., Dalins, J. & Rolan, G., 2020, 2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 186-191 6 p. 9311021


We describe the Artificial Intelligence for Law Enforcement and Community Safety (AiLECS) research laboratory, a collaboration between the Australian Federal Police and Monash University. The laboratory was initially motivated by work towards countering online child exploitation material. It now offers a platform for further research and development in AI that will benefit policing and mitigating threats to community wellbeing more broadly. We outline the work the laboratory has undertaken, results to date, and discuss our agenda for scaling up its work into the future.

AiLECS Lab Launch

Short video describing the motivation and rationale for the lab.

Academic publications
Monte-Carlo Filesystem Search – A crawl strategy for digital forensics

Dalins, Janis, Campbell Wilson, and Mark Carman. “Monte-Carlo Filesystem Search–A crawl strategy for digital forensics.” Digital Investigation 13 (2015): 58-71.


Criminal investigations invariably involve the triage or cursory examination of relevant electronic media for evidentiary value. Legislative restrictions and operational considerations can result in investigators having minimal time and resources to establish such relevance, particularly in situations where a person is in custody and awaiting interview. Traditional uninformed search methods can be slow, and informed search techniques are very sensitive to the search heuristic’s quality. This research introduces Monte-Carlo Filesystem Search, an efficient crawl strategy designed to assist investigators by identifying known materials of interest in minimum time, particularly in bandwidth constrained environments. This is achieved by leveraging random selection with non-binary scoring to ensure robustness. The algorithm is then expanded with the integration of domain knowledge. A rigorous and extensive training and testing regime conducted using electronic media seized during investigations into online child exploitation proves the efficacy of this approach.