The Blackwell-Hart Technical Bulletins
The Blackwell-Hart Technical Bulletins serve as the research and commentary archive for the Blackwell-Hart Methodology™ (BHM™). The archive documents applied observations, case studies, and structured analysis related to AI-assisted discovery systems, authority infrastructure, and entity interpretation.
Blackwell-Hart Methodology™ Technical Bulletin 26-31: Relevance Is Not Recommendation — When an Entity Fits the Question but Is Not Selected
An entity can be relevant to a question without being selected for recommendation. Blackwell-Hart Methodology™ (BHM™) distinguishes retrieval, recognition, interpretation, relevance, and recommendation as separate observable stages, providing a more precise basis for evaluating how AI-assisted discovery systems select entities under defined query conditions.
Blackwell-Hart Methodology™ Technical Bulletin 26-29: Baseline Integrity — Why Change Cannot Be Measured Without a Starting Point
Learn why establishing a documented baseline is essential for measuring changes in AI representation. The Blackwell-Hart Methodology™ explains how comparable observations support reliable measurement while distinguishing evidence of change from proof of causation.
Blackwell-Hart Methodology™ Technical Bulletin 26-29: Baseline Integrity — Why Change Cannot Be Measured Without a Starting Point
As AI systems move from generating answers to taking actions, accountability depends on more than the model itself. Examining the Australian Medicare incident, this article explores access permissions, safeguards, monitoring, incident reporting and evidence-based accountability through the Blackwell-Hart Methodology™ perspective.
Blackwell-Hart Methodology™ Technical Bulletin 26-28: Interpretation Drift — When the Entity Changes Without Anyone Changing the Information
Interpretation drift occurs when AI systems represent an entity differently over time. Blackwell-Hart Methodology™ (BHM™) examines baseline observations, environmental changes, controlled re-observation, and attribution analysis to distinguish measurable changes in representation from assumptions about their causes.