Salthacked/AI-Governance-Readiness-Index-for-Africa--AIGRI--2026

# AIGRI 2026: AI Governance Readiness Index for Africa An academically rigorous, Principal Component Analysis (PCA)-weighted indicator framework evaluating **AI safety and ethics governance readiness** across 30 African countries. AIGRI provides a governance-first, decolonial perspective on AI readiness that prioritizes regulatory capacity, data sovereignty, and civic space over commercial AI capabilities. ## 📊 Quick Start ### View the Dashboard Open `dashboard/index.html` in a modern browser to explore: - **Country rankings** across three governance tiers (Pioneers, Emerging, Lagging) - **Interactive radar charts** showing performance across four pillars - **Regional comparisons** and detailed country profiles - **PCA methodology validation** and econometric analysis ### Run the Data Pipeline ```bash python src/pipeline.py ``` Generates PCA-weighted indicators and exports to `dashboard/data.json`. ### Generate Publication Figures ```bash python src/generate_plots.py ``` Creates high-resolution academic figures in `paper/figures/`. --- ## 📁 Project Structure ``` AIGRI-2026/ ├── dashboard/ # Interactive web application │ ├── index.html # Main dashboard UI │ ├── app.js # Core interactivity & navigation │ ├── data.js # Data loading for local file:// mode │ ├── styles.css # Publication-quality styling │ ├── data.json # Generated: PCA-weighted results │ └── README.md # Dashboard-specific documentation │ ├── data/ # Research datasets & documentation │ ├── raw_indicators.csv # 16 scored indicators (30 countries) │ ├── country_metadata.csv # Macro-level validation variables │ ├── indicator_sources.csv # Data source attribution │ ├── codebook.md # Complete variable dictionary │ └── desk_coding_protocol.md # Scoring rubrics & methodology │ ├── src/ # Data pr …