About
The Project
The Global Representativeness Index (GRI) was developed to address a gap in survey methodology: while response rates and sample sizes are routinely reported, there has been no standardized, quantitative measure of how well a sample represents the global population across multiple demographic dimensions.
As large-scale surveys and public consultations increasingly shape AI policy and governance, the need for transparent representativeness measurement has become critical. The GRI provides researchers, policymakers, and survey practitioners with a rigorous, open-source tool for this purpose.
Citation
If you use the GRI in your research, please cite:
@software{hadfield2025gri,
author = {Hadfield, Evan and Konya, Andrew},
title = {The Global Representativeness Index: Measuring Demographic
Representativeness in Survey Samples Using Total Variation Distance},
year = {2025},
url = {https://github.com/collect-intel/gri},
license = {MIT}
}An arXiv preprint is forthcoming.
License
This project is released under the MIT License. You are free to use, modify, and distribute the code and methodology with attribution.
Data Sources
The GRI benchmarks are derived from the following authoritative datasets:
| Dataset | Source | Year | Description |
|---|---|---|---|
| World Population Prospects | UN DESA Population Division | 2023 | Population by country, gender, and 5-year age group |
| Global Religious Landscape | Pew Research Center | 2010 | Religious composition by country |
| World Urbanization Prospects | UN DESA Population Division | 2018 | Urban/rural population distribution by country |
All benchmark data files and full source attribution are available in the repository under data/raw/benchmark_data/.
Contributing
We welcome contributions. Please open an issue on GitHub for bug reports, feature requests, or questions about the methodology.
Contact
For inquiries about the GRI project or the Global Dialogues survey series, please reach out through GitHub Issues.