Our Work

Case studies and practical examples of ethical AI frameworks in action across Vancouver tech teams.

Bias Audit

Algorithmic Bias Audit & Assessment

Comprehensive evaluation of AI models and datasets to identify potential bias sources and assess fairness across demographic groups. Includes detailed audit documentation for stakeholder reporting and compliance readiness.

Covers dataset analysis, model fairness metrics, demographic parity testing, and audit documentation practices.

Workshop

Ethical AI Design Workshop

Hands-on workshop for development teams covering ethical considerations in AI system design, stakeholder impact mapping, and fairness-by-design principles. A 12-week programme built around weekly practical sessions with real system examples.

Includes fairness principles, stakeholder analysis, design patterns for ethics, and impact assessment methodologies.

Data scientist monitoring model fairness metrics

Strategy

Bias Mitigation Strategy Development

Customized approach to address identified biases in your AI systems, including data collection improvements, algorithmic adjustments, and long-term governance frameworks. Designed for ongoing fairness monitoring and team accountability.

Focuses on practical implementation over 6-8 weeks with deliverables for data practices, model adjustments, and monitoring setup.

Governance

Responsible AI Governance Framework

Build organizational structures and decision-making processes for responsible AI development. We help teams establish clear accountability, documentation practices, and review processes that scale with your AI projects.

Includes governance templates, decision-making frameworks, and documentation standards tailored to your organization's size and stage.

Ready to strengthen your AI practices?

Let's talk about your specific challenges and how we can help your team build fairer, more transparent AI systems.

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