Audit AdventuresEquilibrium Fairness: the companion reference implementation is live
A few days ago I published my working paper: Equilibrium Fairness: A Runtime Governance Architecture for Monitoring and Correcting Fairness Drift in High-Impact AI Systems (DOI: 10.5281/zenodo.20547396).
Today I published the companion reference implementation.
One of the challenges in AI governance is that many frameworks stop at principles. We talk about:
- Monitoring
- Human oversight
- Accountability
- Fairness
But organisations still need practical mechanisms that connect those principles to deployed systems.
The reference implementation demonstrates a simple runtime governance loop: E₀ (Initialise) → δ (Monitor Drift) → θ (Threshold Check) → κ (Escalation Event).
The prototype includes:
- Fairness drift monitoring
- Threshold breach detection
- Structured escalation events
- Configuration examples
- Tests
- HTML reporting
It is intentionally small and designed as a research-oriented reference implementation rather than a production system.
The objective was not to build a SaaS platform. The objective was to demonstrate that the architecture described in the paper can be executed end-to-end.
Feedback, critique and discussion are welcome. Get in touch or open an issue on the repository.