When a Risk Label Hides the Evidence
TL;DR for operators An AI system can produce mutually consistent risk labels, supporting evidence, and explanatory factors while still hiding relevant information. In a 2026 study by Tianle Hu and coauthors,1 a classification rule suppresses supporting evidence whenever a particular risk category is predicted—even if that prediction is wrong. The study also demonstrates a modest improvement from combining two different psychosocial factor verifiers: Macro F1 rises from 0.5467 for the stronger standalone model to 0.5562. For product teams, the findings favor separate monitoring of classification errors, suppressed evidence, and factor-level reliability. They do not establish that the resulting explanations are clinically trustworthy. ...