Teaching
Learning technology to make better public decisions
Applied university courses bridging data analysis, information systems, critical thinking, prototyping, and rigorous human review of artificial intelligence.
Pedagogical Approach
Technology in government is taught as part of a coherent decision-making chain: problem definition, evidence gathering, critical interpretation, alternative design, tooling, and democratic accountability.
Course assignments require empirical evidence, transparent documentation of methods, and explicit explanation of criteria. While generative AI is leveraged as an analytical workbench, all decisions and submissions remain under rigorous human review.
Core Principles
- Understand before automating: Grasp the administrative challenge before writing code or configuring algorithms.
- Explain how the result was obtained: Complete auditability and reproducibility of data pipelines.
- Distinguish data, interpretation, and decision: Raw figures do not make policy on their own.
- Design accessible and understandable tools: Solutions must serve real public servants and citizens.
- Assess risks, limits, and institutional responsibilities: Address algorithmic bias, data privacy, and legal frameworks.