AI-driven early warning for community violence

This lecture explores how artificial intelligence (AI) can support community‑level violence prevention by enabling early detection of risk factors and proactive interventions. It outlines challenges in prevention, including delayed detection, limited data insights, and research‑practice gaps. A proof‑of‑concept system using multi‑source data (incident reports, social indicators, anonymised mobility data) to generate early warnings is presented. Ethical, legal, and operational considerations ─ such as privacy, bias, trust, and transparency ─ are discussed. Preliminary pilot results illustrate how AI outputs were integrated into a local prevention network, used by practitioners, and the lessons learned regarding stakeholder engagement and workflow integration. The lecture concludes with guidance for responsibly designing AI‑augmented early warning mechanisms and bridging technology with practical prevention.