Human trafficking impacts millions globally, particularly women, girls, and marginalized communities. The U.S. State Department's Trafficking in Persons (TIP) Reports are a key data source for tracking global human trafficking but may contain political biases.

This study examines how narratives translate into rankings, questioning whether these rankings reflect objective assessments or political influences. We use supervised machine learning to analyze this process and distinguish between bias from changing standards versus direct political intervention. Our findings reveal that political factors have a stronger influence on the final rankings than evolving data collection standards.