SamBob – Implementing and scaling up an AI-based cross-sectoral approach for innovative and effective crime prevention for children and youth

Purpose and goal

SamBob involves the implementation of an AI-based cross-sectoral approach that strengthens the accuracy of crime prevention efforts for children and youth who are at risk of being drawn into organized crime. The goal is proactive, individually tailored solutions which lead to better results for children and young people. In this phase, the approach is deepened and spread to more municipalities, with the goal of creating a national model for data-driven, cross-sectoral crime prevention work.

Expected effects and result

In the short term, a working method is implemented that supports data-driven decisions/follow-up. In the medium term, the innovation capacity is strengthened, which leads to more individually adapted efforts and stronger collaboration. This creates a culture of learning instead of method focus. In the long term, reduce recruitment to organized crime. The project lays the foundation for a national model where AI-support and cross-sectoral working methods are used to address societal challenges.

Planned approach and implementation

The project is run in close collaboration between authorities, the civil society, business, researchers. Municipalities and civil society contribute knowledge, experience and work teams who implement and test the tool. Researchers are responsible for analysis, evaluation and learning on a scientific basis, while the business community develops AI tools and working methods. All actors are involved through regular workshops, digital meetings and ongoing reconciliations