Both developments come as Audit Adventures continues its work helping organisations operationalise responsible AI governance, audit readiness and compliance with emerging AI regulatory frameworks.
The VerifyWise AI Governance Directory is a curated international directory of AI governance consultants, EU AI Act advisers, responsible AI specialists and training providers. At the time of writing it lists 606 providers across 38 countries, organised around frameworks including ISO/IEC 42001, the NIST AI Risk Management Framework and emerging AI regulatory requirements.
Audit Adventures has also joined the AI Verify Foundation, an international open-source community focused on building AI testing tools, frameworks, standards and good practice for trustworthy AI.
Maphi Bayolo, founder of Audit Adventures, said: “Being added to the VerifyWise AI Governance Directory and joining the AI Verify Foundation are important milestones for Audit Adventures. Responsible AI is no longer something organisations can treat as an abstract principle; they need practical ways to demonstrate governance, oversight, monitoring and accountability. Nobody governs AI alone, and these memberships place Audit Adventures within communities that are actively working to make trustworthy AI practical.”
Audit Adventures is developing structured implementation journeys that help organisations understand and apply AI governance frameworks such as the EU AI Act and ISO/IEC 42001. Its work turns responsible AI principles into practical workflows, including risk management, documentation, control mapping, human oversight and ongoing monitoring.
Equilibrium Fairness is built on the idea that fairness should not be treated as a one-time assessment at model launch. Instead, it should be monitored over time, because AI systems can drift as data, populations and use cases change.
The reference implementation uses a four-stage loop: initialise, monitor, threshold and correct. It measures fairness drift using signals such as the demographic parity gap and equalised odds gap, compares those signals against pre-set thresholds, and logs structured escalation events when a threshold is breached. Correction remains a human responsibility.
Bayolo added: “Organisations increasingly need evidence that AI systems are being monitored, questioned and improved after deployment. Equilibrium Fairness is intended to support that need: a practical governance layer that compliance, audit, model-risk and legal teams can understand, configure and evidence.”
The framework is being developed to align with the direction of AI regulation and standards, including lifecycle risk management, human oversight, robustness and ongoing evaluation.
Audit Adventures plans to continue developing its governance platform and the Equilibrium Fairness framework to support organisations seeking practical, evidence-based approaches to responsible AI compliance.
