AI Governance and Organizations
How organizations adopt, govern, monitor, and remain accountable for AI systems across their lifecycle.
AI adoption
Accountability
Regulation
Research
Research
I study how organizations govern AI and digital technologies, and how governance requirements can become organizational capabilities and system design.
Research Focus
Three Connected Areas
My work moves between organizational questions, governance mechanisms, and computational or design-oriented evaluation.
Governance by Design
How selected governance requirements can be translated into organizational and technical mechanisms and evaluated in practice.
Design Science
Traceability
Verification
Computational Inquiry
Using computational and quantitative methods when they strengthen the research question and empirical design.
NLP & ML
Networks
Causal methods
Selected Work
Current Research
Two streams currently anchor the broader research agenda.
Design Science Research
Healthcare AI
Governance by Design in Federated Healthcare AI
Research on inter-organizational AI collaboration where sensitive data remain local while participation, traceability, verification, and accountability are governed through the system architecture.
Federated learning
Blockchain
Privacy-preserving verification
Cybersecurity
Doctoral Research
Cybersecurity Governance as an Adaptive Control Capability
Research on how firms translate regulatory obligations into governance capabilities, with attention to sensing, action, learning, and organizational control.
Longitudinal text analysis
Quasi-experimental inference
Causal ML
Research Logic
From Organizational Problems to Evaluated Design
A recurring logic in my work is to move from context and governance requirements to design or organizational mechanisms, then evaluate what those mechanisms can and cannot achieve.
Methods
Analytical Toolkit
Information Systems theorizing
Design Science Research
Policy analysis
Regression & panel analysis
Causal inference
NLP & machine learning
Network analysis
Python & R