Strategic AI Project
Somali Social Stability AI (SSAI)
A strategic National AI initiative focused on detecting harmful Somali-language content, reducing digital incitement, and strengthening social stability through responsible language intelligence systems.
Project Overview
Somali Social Stability AI project is designed to identify harmful patterns in Somali digital content, including hate speech, tribal incitement, political misinformation, and violence-triggering language. The project combines language technology, responsible AI, and social stability analysis to support peacebuilding, media responsibility, and early-warning systems.
How It Works
Phase 1: Build labeled dataset
The first stage is to build a high-quality Somali-language dataset with clearly defined content categories including neutral content, hate speech, tribal incitement, political misinformation, and violence-triggering content. University students can support labeling through structured annotation workflows.
Phase 2: Train classification model
Transformer-based models such as BERT can be fine-tuned on Somali text to classify harmful content. This enables the system to understand context and detect risk signals in digital discourse.
Phase 3: Deploy system
The system can be deployed as an API for Facebook pages, media companies, and community platforms. It can flag risky content and suggest neutral rephrasing in real time.
Advanced Layer: Conflict Risk Index
A powerful extension of SSAI is the development of a Conflict Risk Index. This system detects rising hate trends, geographic hotspots, and language linked to violence, enabling early intervention.
Rising hate trends
Track increases in dangerous language patterns.
Geographic hotspots
Identify regions with rising digital risk.
Violence-linked keywords
Detect clusters of harmful phrases.