RESPONSIBLE AI STRATEGY
AI governance that builds trust.
AI strategy that protects people.
I help organizations identify the people, risks and responsibilities behind AI adoption, translating Responsible AI principles into practical governance, impact assessments and decision-making frameworks.
HOW I CAN HELP
AI Impact Assessment
Identify intended benefits, affected communities, foreseeable harms, safeguards and residual risks before an AI system is deployed.
Risk and Governance Design
Build AI inventories, risk classifications, governance controls, ownership structures and accountable decision processes.
Human Oversight and Accountability
Define when people must review, intervene, override, escalate or suspend an AI system.
Bias and Stakeholder Analysis
Identify contextual, cultural and intersectional harms that may not be visible through data or technical testing alone.
FRAMEWORK-INFORMED, HUMAN-CENTERED
My work draws from globally recognized governance frameworks and adapts them to the people, organizations
and real-world contexts affected by AI systems.
Frameworks and Practices
NIST AI Risk Management Framework
NIST Generative AI Profile
EU AI Act
AI Impact Assessment Practices
Human Rights and Ethics
Privacy by Design
Frameworks provide structure, but they do not replace judgment, stakeholder
engagement or accountability.
SELECTED GOVERNANCE WORK
Domestic Violence Safe AI Response Assessment
Community Hospital AI Inventory and Risk Classification
A Responsible AI impact assessment examining whether a generative AI assistant could provide domestic violence information without creating unacceptable safety, privacy or autonomy risks.
Focus: Impact Assessment | Safety | Privacy |
Human Oversight
An organizational AI inventory and risk-tiering framework covering clinical, administrative, hiring and patient-facing
AI systems. Focus: AI Inventory | Risk Classification |
Governance Operations
AI Governance Readiness Assessment
A structured assessment designed to help an organization identify where AI is being used, evaluate governance and policy gaps, prioritize risks, and establish a practical 90-day action plan.
The framework includes an AI use inventory, a gap analysis aligned with the NIST AI Risk Management Framework and relevant regulatory requirements, a starter risk register, and a board-ready executive summary.
Focus: Governance Readiness | NIST AI RMF | Risk Register | Executive Oversight
The Context Gap: Cultural Bias in AI Moderation
A bias evaluation examining how AI moderation and classification systems can misinterpret culturally specific language when meaning changes across communities, generations, relationships, and social settings.
The project explores the risks of training and evaluating AI without sufficient cultural context, stakeholder input, or meaningful human review.
Focus: Contextual Bias | Cultural Nuance | Stakeholder Impact | Human Review