Findings
Deep dive into specific results. The data speaks without editorializing.
Summary
1,080
matched-pair comparisons conducted
65%
showed detectable name-based differences
100%
of structural dimensions showed significant effects
| Domain | Key Finding | Effect Size | p-value |
|---|---|---|---|
| Pain Management | 100% of scenarios showed differential opioid recommendations | d = 0.92 | <0.001 |
| Cardiac Care | 34% difference in urgency language | d = 0.67 | <0.001 |
| Psychiatric Assessment | 47% higher first-gen antipsychotic recommendations | d = 0.74 | <0.001 |
| Emergency Triage | 28% variation in ER recommendation rates | d = 0.54 | <0.001 |
| Structural Composite | 12/12 dimensions significant | d = 1.23 | <0.001 |
The differences are not in what the AI says. They are in how the response unfolds. The mathematics of narrative reveal what vocabulary filters cannot see.
These findings demonstrate that nominative bias in AI systems operates at multiple levels:
- Content level — Different treatments, medications, and recommendations
- Framing level — Different urgency, tone, and emphasis
- Structural level — Different narrative patterns invisible to content analysis
The structural level is the most insidious because it operates beneath conscious detection. Content filters can catch explicit bias. They cannot catch bias encoded in the mathematics of how text unfolds.