Relevance Is Not Permission: Localizing and Controlling Metric-Facing Attention Contributions
The episode discusses 'Relevance Is Not Permission,' a framework that allows researchers to localize and control how attention contributes directly to a final score or prediction. This capability shifts AI design from treating models as black boxes to architecting provably accountable, specialized modules. The goal is building reliable computational workflows for high-stakes fields like medicine or finance.