The concepts of “label” and “hard negative” are central to understanding how artificial intelligence (AI) models, specifically those in the domain of supervised learning, are developed and evaluated. These concepts are fundamentally intertwined with how data is prepared, how models learn from data, and how their strengths and weaknesses are subsequently assessed. 1. The Concept