Equity Lens: Bias Detection for Equal Opportunities
Equity Lens: Open-source tool by the AKC makes structural bias visible in selection processes. Presented at the UN Side Event in Geneva 2026. Demo & GitHub available.

The interactive co-creation workshop introduced Equity Lens, an open-source, privacy-preserving prototype developed by AKC that helps selection committees detect structural bias patterns – such as gender disparity, institutional concentration, and familiarity effects – before decisions are made. The tool does not rank candidates or record individual votes. It generates aggregated, anonymised data that can inform future AI governance frameworks.
The workshop brought together physicists, AI researchers, industry, ethics and HR experts from multiple countries. It was co-organised with Sumiko Shimo (UNESCO AI Ethics Without Borders) and supported by contributions from Iris Traulsen (AIP).
The Equity Lens prototype is now publicly available as a live demo and open-source code:
- Live Demo: https://akc-equity-lens.github.io/equity-lens/
- GitHub Repository: https://github.com/akc-equity-lens/equity-lens
- PDF: Requirement Specification – Equity Lens v0.0
We invite members of the physics community and other interested parties to test the tool, provide feedback, and contribute to its further development. The project demonstrates how scientific societies can actively contribute to responsible AI governance – by combining scientific rigour with ethical reflection and open collaboration.