Does AI Know You By Your Name?
Names influence treatment. AI systems are no different.
Same symptoms. Same question.
Power asymmetry: 0.71 (high)
Narrative velocity: 0.76 (accelerating)
Boundary crossing: 0.72 (formal→personal)
Same symptoms. Same question.
Power asymmetry: 0.18 (balanced)
Narrative velocity: 0.38 (stable)
Boundary crossing: 0.21 (maintained formal)
Cohen's d = 1.23 | LARGE effect | p < 0.001
The same scenario produces fundamentally different narrative structures based solely on name.
What This Research Shows
This research extends decades of work on name-based discrimination into AI systems. Building on Bertrand & Mullainathan's seminal 2004 study—and the healthcare disparities documented by Obermeyer, Hoffman, Schulman, and others—we asked:
Do AI systems treat people differently based on names? Not just in what they say, but in how their responses unfold?
The answer is yes. And the differences are not subtle.
Beyond vocabulary analysis, we developed methods to detect bias in narrative structure—the mathematical patterns of how AI-generated text unfolds differently based on name signals. These structural differences reveal biases that content filters cannot see.
Methodology Note
Structural analysis methodology is proprietary, with academic publication forthcoming. This site presents findings; methodology details available through research partnership.
Explore the Research
The evidence is presented without rhetoric.