CANON Named Owner Principle · every clinical AI deployment requires two named persons in the audit trail — a Governance Owner at Layer 4 and a Decision Owner at the bedside, not one substituting for the other WORKING PAPER №01 The handoff that isn’t · how clinical AI escapes accountability · Mo Johnson, MD MBA EVIDENCE Duke-Margolis 2026 · most US health systems have not named who owns the clinical AI decision when something goes wrong CANON Layer 4 · Clinical AI Governance at the bedside · the layer where the named owner has to live FRAMEWORK Clinical AI Accountability Canvas™ · the diagnostic framework distinguishing Clinical AI Governance from General AI Governance EVIDENCE Stanford MedAgentBench · agentic systems already executing clinical recommendations without a named adjudicator on the chart CANON The Two Inputs · internal data the institution audits · external data the model was trained on, rarely audited at the deployment site CITATION MedicoVigilance™ Issue 6 · The Layer With No Name · 1,627 institutional subscribers CANON The Accountability Gap · the structural failure point where AI stops and the physician starts FRAMEWORK Mind the 9 Blocks™ · the nine institutional blocks that must be in place before clinical AI deployment EVIDENCE npj Digital Medicine · the four-layer governance cascade · most institutions have built the first two layers and left Layer 4 unbuilt PORTFOLIO GPe Research · adjudication infrastructure for regulated sectors, beginning with clinical AI CANON Named Owner Principle · every clinical AI deployment requires two named persons in the audit trail — a Governance Owner at Layer 4 and a Decision Owner at the bedside, not one substituting for the other WORKING PAPER №01 The handoff that isn’t · how clinical AI escapes accountability · Mo Johnson, MD MBA EVIDENCE Duke-Margolis 2026 · most US health systems have not named who owns the clinical AI decision when something goes wrong CANON Layer 4 · Clinical AI Governance at the bedside · the layer where the named owner has to live FRAMEWORK Clinical AI Accountability Canvas™ · the diagnostic framework distinguishing Clinical AI Governance from General AI Governance EVIDENCE Stanford MedAgentBench · agentic systems already executing clinical recommendations without a named adjudicator on the chart CANON The Two Inputs · internal data the institution audits · external data the model was trained on, rarely audited at the deployment site CITATION MedicoVigilance™ Issue 6 · The Layer With No Name · 1,627 institutional subscribers CANON The Accountability Gap · the structural failure point where AI stops and the physician starts FRAMEWORK Mind the 9 Blocks™ · the nine institutional blocks that must be in place before clinical AI deployment EVIDENCE npj Digital Medicine · the four-layer governance cascade · most institutions have built the first two layers and left Layer 4 unbuilt PORTFOLIO GPe Research · adjudication infrastructure for regulated sectors, beginning with clinical AI
TOPIC

Accountability

Accountability, in the GPe Research framework, is the institutional condition in which a named person bears identifiable responsibility for a specific decision or outcome. It is distinct from transparency (the ability to inspect a system), explainability (the ability to understand a model's reasoning), and compliance (adherence to regulatory checklists). Accountability requires a human owner who cannot disclaim responsibility through reference to an algorithm. The Named Owner principle operationalizes this condition.

PAPERS ON THIS TOPIC