By Romi Sigsworth 

Artificial intelligence (AI) offers innovative solutions for addressing a range of illegal activities that impact Africa’s environment. This report explores how AI is being used in Africa to provide intelligence on organised environmental crime, craft tools to assess its impact, and develop methods to detect and prevent environmental criminal activities. It discusses the challenges and opportunities AI poses for policing environmental crime in Africa, and proposes recommendations that would allow AI-powered policing to make a real difference on the continent. Recommendations • African governments and organisations should invest in gathering large, local data sets to allow AI models to produce appropriate and relevant solutions. • Investments in digital and communication infrastructure need to be made across Africa to improve and expand access to and affordability of AI solutions. • Police forces across Africa should include technology and AI skills capacity building into their basic and professional development training curricula. • Guardrails should be established through legislation to protect data, ensure privacy where necessary, and regulate the use of AI. • Public-private partnerships must be strengthened for law enforcement agencies across Africa to receive the technology and training they need to effectively embed AI tools into their methodologies to combat environmental (and other) organised crime

Enact Africa 2024. 28p.

By Clio Andris, Daniel Della Posta, Brittany N. Freelin, Xi Zhu, Bradley Hinger, Hanzhou Chen

The American Mafia is a network of criminals engaged in drug trafficking, violence and other illegal activities. Here, we analyze a historical spatial social network (SSN) of 680 Mafia members found in a 1960 investigatory dossier compiled by the U.S. Federal Bureau of Narcotics. The dossier includes connections between members who were ‘known criminal associates’ and members are geolocated to a known home address across 15 major U.S. cities.

Under an overarching narrative of identifying the network’s proclivities toward security (dispersion) or efficiency (ease of coordination), we pose four research questions related to criminal organizations, power and coordination strategies. We find that the Mafia network is distributed as a portfolio of nearby and distant ties with significant spatial clustering among the Mafia family units.

The methods used here differ from former methods that analyze the point pattern locations of individuals and the social network of individuals separately. The research techniques used here contribute to the body of non-planar network analysis methods in GIScience and can be generalized to other types of spatially-embedded social networks.

International Journal of Geographical Information Science Volume 35, 2021 - Issue 12: Spatial Social Networks in GIScience