Responsible AI & AI Governance
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
Survivor-Centered Responsible AI Governance
An ongoing Responsible AI governance project examining how generative AI systems respond to domestic violence disclosures. The assessment considers whether AI can provide useful information without increasing danger, compromising privacy, undermining survivor autonomy, or substituting for trained human support.
The work focuses on identifying foreseeable harms, defining safe response boundaries, determining when human intervention or referral is necessary, and establishing safeguards for high-risk conversations.
Focus: AI Impact Assessment | Survivor Safety | Privacy | Human Oversight | Harm Prevention | Safe Response Design
Demonstration Governance Engagements.
Enterprise AI Governance Case Studies
Detailed fictional organizational scenarios developed to demonstrate applied AI governance assessment, risk management, control design, evidence, monitoring, and executive decision-making.
BrightBridge Learning International
International Children’s Education Nonprofit
A simulated enterprise AI governance engagement examining AI used across learning, safeguarding, translation, communications, and program operations within an international children’s education organization.
The assessment identifies AI systems, classifies risk, evaluates potential impacts on children and other stakeholders, establishes governance controls, and develops monitoring and remediation requirements.
Focus: AI Inventory | Risk Classification | Child Impact Assessment | Risk Register | Controls & Evidence | Governance Roadmap
Meridian Talent Technologies
Global HR Technology Company
A simulated AI governance engagement examining AI-enabled hiring and workforce technologies, including candidate screening, ranking, matching, and employment decision support.The assessment evaluates potential discrimination risks, dataset limitations, transparency, human review, candidate recourse, governance controls, and ongoing monitoring requirements.
Focus: Employment AI | Bias & Fairness | Dataset Risk | Transparency | Human Oversight | Monitoring
CedarBridge Financial
Consumer Financial Services Company
A simulated enterprise AI governance engagement examining AI used in consumer credit decisions, fraud detection, customer service, marketing, and operational workflows. The assessment focuses on model governance, third-party AI risk, explainability, control effectiveness, evidence requirements, risk appetite, monitoring, and executive accountability. Focus: Model Governance | Third-Party AI Risk | Controls & Evidence | Risk Appetite | Monitoring | Executive Oversight
NorthStar Health Network
Regional Hospital System
A simulated clinical AI governance engagement examining AI used across clinical care, patient communication, operational planning, revenue-cycle management, and internal workforce tools. The assessment evaluates patient-safety risks, clinical human oversight, health-data governance, vendor risk, model monitoring, incident response, and executive accountability.
Focus: Clinical AI Governance | Patient Safety | Human Oversight | Health Data Privacy | Vendor Risk | Model Monitoring