Press

Lina Song is a Decision Architect and Founder & CEO of Doogooda, building auditable decision systems that turn data into defensible actions in high-stakes, regulated environments. Her work focuses on decision-making under uncertainty, accountable AI in deployment, and operational governance in healthcare and public systems.

Lina Song is a Decision Architect and the Founder & CEO of Doogooda, where she designs auditable decision systems that translate data into defensible actions under real constraints—capacity, staffing, budgets, and policy. Her approach combines causal reasoning, scenario simulation, and optimization as decision infrastructure that can be explained, stress-tested, and audited. She previously held an academic appointment in operations/technology and has worked across healthcare and public-sector decision contexts. Lina speaks and writes about decision-making under uncertainty, accountable AI in deployment, and why operations is fundamentally a governance problem in high-stakes institutions.

Lina Song is a Decision Architect and the Founder & CEO of Doogooda, a decision-intelligence company focused on auditable systems that help institutions make defensible choices under uncertainty. Rather than treating AI as prediction, she builds decision infrastructure: explicit assumptions, scenario simulation, constraint-aware optimization, and governance-ready outputs that teams can justify, execute, and audit. Her work is grounded in real operational constraints—capacity, staffing, budgets, and policy trade-offs—especially in regulated, high-stakes environments such as healthcare and public systems. Lina previously served in an academic role in operations and technology and has worked across research and applied settings. She speaks and writes about the practical meaning of "accountable AI," the politics of AI infrastructure, and why many operational problems are ultimately governance problems. Her core themes include trade-offs and incentives, decision quality under uncertainty, and designing systems that remain credible when stakes are high and accountability is non-negotiable.

Decision Architecture for Uncertain Times

A framework for structuring organizational decisions when predictions fail. Covers trade-off mapping, assumption documentation, and governance design.

Auditable AI: Beyond Explainability

What institutions actually need from AI systems, and why current approaches fall short. A practical framework for accountability.