Salthacked/AI-Governance-Readiness-Index-for-Africa--AIGRI--2026
Type de record:
project
Créateur:
Sal
Hôte:
# 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 …