I build, test, and audit AI systems that are safe, fair, and production-ready. My work sits at the intersection of AI safety, fairness engineering, and regulatory compliance. I turn the gap between "AI principles" and "production reality" into structured, actionable practice. I bring a quality engineering foundation to this: systematic testing, regression thinking, and production reliability applied to the harder problem of AI system behaviour.
I created and maintain the Fairness Assessment, Action & Implementation Playbook series. Practitioner-first frameworks for evaluating, correcting, and delivering AI systems in real product environments. Built openly for teams who need to move from principles to production-ready checks.
I conduct structured red teaming exercises: adversarial input testing, edge case probing, failure mode mapping, and model behaviour stress-testing. Built to surface risks before they reach users.
• Audit & Testing: bias detection, distribution shift, explainability, drift monitoring
• Regulatory Alignment: EU AI Act · GDPR Art. 22 · NIST AI Risk Management Framework · ISO/IEC 42001 · ICO · HIPAA
• Team Enablement: governance workflows that make engineering teams sustainably effective
Built for practitioners doing the actual work of responsible AI.
