Applying industry standard responsible AI frameworks, ensuring AI governance is applied to AI models with the inclusion of an overall responsible strategy which includes fairness, safety, data governance, humans-in-the-loop, privacy preserving ML methods, security, sustainability measures and risk assessments.

Evaluate AI models for regulatory compliance and associated AI risks, including systemic risks. Setting organisations up for regulation readiness (e.g., GDPR, EU AI Act, IEEE AI Ethics Standards).

Develop transparency and explainability frameworks. This helps towards transparency on AI decisions, making it more interpretable for businesses and policymakers.

Advising governments, enterprises, and NGOs on AI governance frameworks and responsible AI development and deployment.

Using data ethics and credible data curation methods to build datasets that are fair, representative, and inclusive, while applying community participatory research methods and synthetic data to reduce bias in AI models and provide control to organisations.

Implement guardrails to prevent misinformation, hallucinations, and adversarial AI threats in LLMs and generative AI models.

Address AI-driven discrimination, human rights violations, and human-centred AI policies using human rights impact assessments.

Provide educational programmes including training and workshops to employees offering AI literacy skills and practical knowledge on AI and responsible AI.

Speak at events and offer insights on responsible AI practices and frameworks, AI governance, and risk assessments.