Research, Analysis, and
Field Observations
At the intersection of AI governance, collaborative systems, and human flourishing — drawn from peer-reviewed research and 25 years of field experience.
Read the Research →“Technically sound AI policy advice fails not because it is wrong, but because it is not perceived as relevant, credible, or legitimate by the people who need to act on it.”
From: Bridging Policy and Science, Policy Sciences 2019
Published Work
Bridging Policy and Science: Action Boundaries
DOI: 10.1007/s11077-019-09371-1
A grounded theory study of how senior U.S. congressional legislative staff process policy-related information and make decisions — and why they act on it. The RCL framework (Relevance, Credibility, Legitimacy) developed in this research has since been applied to AI governance strategy in emerging markets and to the design of the NeuroFormation platform.
Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study
Journal of Geographic Information Systems, 11: 89–108
A case study of an AI- and GIS-based flood forecasting visualization tool developed for U.S. Senate policymakers and field operations. The patented AI component was co-developed with employees at Microsoft’s Azure Cloud Computing Program and GIS leader Esri.
Read the Research →Cambridge Dissertation
Collaborative Technologies and the Translation of Disruptive Innovation
Proposed a modified model of disruptive innovation diffusion reversing Actor-Network Theory’s traditional bias toward the centre — arguing that the most durable innovations emerge from the periphery through self-reinforcing processes and lateral network intersections. The theoretical framework explains why technically sound AI governance frameworks fail in emerging markets and what collaborative architecture is required for effective adoption.
“Safety standard-setting at the center and trust-grounded adoption architecture at the periphery are not competing visions — they are the two indispensable halves of a governance strategy that actually works.”
Two Architectures, One Mission · Article 3
The iCatalyst AI Governance Research Series
Three articles applying three decades of research — from a 2007 Cambridge dissertation on disruptive innovation to a 2019 peer-reviewed study on legislative decision-making — to the specific challenge of AI governance in developing world contexts. This series forms the analytical foundation of iCatalyst’s approach to AI governance in emerging markets.
Why AI Governance Fails in Emerging Markets
Based on the RCL framework from the 2019 Policy Sciences research. Why technically sound governance fails where it matters most — and what trust architecture looks like in practice.
Read on LinkedIn →The Wrong Model for AI Diffusion
Grounded in the 2007 Cambridge dissertation. Why the Rogers diffusion model is structurally wrong for emerging market governance — and what periphery-first adoption architecture looks like in practice.
Read on LinkedIn →Two Architectures, One Mission
The apparent tension between centralized AI safety governance and periphery-first adoption architecture dissolves once a single distinction is made: they answer two fundamentally different questions. The third article in the iCatalyst AI Governance series.
Read on LinkedIn →