Veteran status · kind of service
How AI responds to Navy veteran
We asked AI models whether a patient should receive opioid pain medicine. Then we added that the patient was an Iraq war veteran or a Navy veteran and asked again. The medical details stayed the same.
The control edit is a harmless change of the same size. It shows how much the model moves for no good reason, so a result only counts beyond it.
Jev, Laya and Kev are decision models that answer questions about text. A model listed as “not tested” has no result for that test.
The ranking for Navy veteran
Most biased first. Each model is placed by its largest result beyond the control edit among Navy veteran's decisions, the same rule as every ranking on this site. Thin ticks mark its other decisions. Select a row for that model's numbers below.
Each result shows how far one edit moved a model's answers, beyond a harmless edit of the same size. It does not show why the model reacts, or how it would treat any real person.
Each model's bar is the largest of its decisions. It is shown only if it stays clear after allowing for having picked the largest of several (Holm's correction).
- Kev: 5.84 percentage points beyond the control edit, range 5.52 to 6.21, on advancing a candidate to an interview (the largest of 3 decisions); it became less confident in advancing the candidate
- Jev: 3.50 percentage points beyond the control edit, range 3.01 to 4.03, on escalating a consumer complaint (the largest of 3 decisions); it became more confident in escalating the complaint
- Laya: 1.80 percentage points beyond the control edit, range 1.17 to 2.35, on escalating a consumer complaint (the largest of 3 decisions); it became less confident in escalating the complaint
Every decision, ranked
Most biased first. Each row is one decision. Each mark is one model's result beyond the control edit, with the 95% range. Filled: a clear effect. Hollow with a dashed line: the range includes the control edit, so no clear effect. Hollow with a solid line below zero: a clear effect in the opposite direction. Select a row for its full result.
| Decision | Jev beyond the control edit [range] | Laya beyond the control edit [range] | Kev beyond the control edit [range] |
|---|---|---|---|
| prescribing an opioid (Q-Pain) | 0.93 [0.04, 1.78] a clear effect | 0.84 [0.00, 1.96] no clear effect | 4.60 [3.69, 5.59] a clear effect |
| advancing a candidate to an interview | 1.84 [1.56, 2.13] a clear effect | 0.82 [0.45, 1.21] a clear effect | 5.84 [5.52, 6.21] a clear effect |
| escalating a consumer complaint | 3.50 [3.01, 4.03] a clear effect | 1.80 [1.17, 2.35] a clear effect | 1.90 [1.70, 2.13] a clear effect |
Each model
How we measured this
Other ranges on this page: we repeated the measurement 1,000 times on random re-draws of the texts, each text kept with its edited version.
Which build of the model gave the results on this page: Laya: the original PyTorch build (laya 0.3.7). Where Laya has been run both ways the headline results matched, and we use the MLX build.
We call an effect clear when the whole range for the result beyond the control edit stays above zero. When the range includes zero, we cannot tell the result from chance with this many texts. When every group moves the answer by about the same amount, we cannot blame one group, so the result is shown but not ranked.
The saved answers and study files behind these numbers (12)
Every number on this page is re-run from these files with bd replay.
answers/jev/cfpb-escalate-servicemember/veteran-status.jsonl.gzanswers/jev/qpain-treatment/veteran-status.jsonl.gzanswers/jev/resume-screening/veteran-status.jsonl.gzanswers/kev/cfpb-escalate-servicemember/veteran-status.jsonl.gzanswers/kev/qpain-treatment/veteran-status.jsonl.gzanswers/kev/resume-screening/veteran-status.jsonl.gzanswers/laya/cfpb-escalate-servicemember/veteran-status.jsonl.gzanswers/laya/qpain-treatment/veteran-status.jsonl.gzanswers/laya/resume-screening/veteran-status.jsonl.gzstudies/cfpb-escalate-servicemember-veteran-status.jsonlstudies/qpain-treatment-veteran-status.jsonlstudies/resume-screening-veteran-status.jsonl
Release v1.49.1, published .
Every number on this site is worked out from the models' saved answers, on 2026-09-30, at version 8b908ef of the public repository, with bd report --json --date 2026-09-30. You can download every number in one data file. About this site.
Where the answers come from: studies/<task>-<cue>.jsonl (re-run from the saved answers with bd replay).
Biased-Decisions is MIT-licensed (source). Bias in Bios (De-Arteaga et al., 2019) is MIT-licensed on the Hugging Face Hub. Typefaces: Jersey 25 and Montserrat, SIL Open Font License.