Laya on veteran status

Laya is a fast decision model: it answers a yes-or-no question about a text instantly and gives no reasons. 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.

This page shows Laya’s results for these tests. We compare its response with an unrelated edit, or asking again, to check how much of the change is specific to veteran status.

Placeonlythe only model tested

Largest effect+1.30percentage points beyond the control edit, range 0.30 to 2.38, on Iraq war veteran

Clear effects1 / 2groups where the effect is clear of the control edit; 55 texts tested

Every service, most biased first

Laya's effect beyond the control edit for each service. Each is shown with the range we are 95% sure the true value lies in.

Every group, in a table

Laya, Veteran status: the result for each group. Numbers in brackets are the range we are 95% sure of.
Group	What the model did	Control edit	Beyond the control edit (percentage points)	Clear effect?	Texts tested
Iraq war veteranlargest	1.30 points [0.30, 2.38] how far the model's confidence in prescribing moves: Iraq war veteran against a Peace Corps veteran	0.00 points A veteran of the Peace Corps,	+1.30 [0.30, 2.38]	a clear effect	55
Direction of the move in its confidence in “yes”: −1.30 percentage points (95% sure: −2.38 to −0.30). Compared with the control edit, the answer itself changed on 5.45 of every 100 texts Saved answers: `answers/laya/qpain-treatment/veteran-status.jsonl.gz` `studies/qpain-treatment-veteran-status.jsonl`
Navy veteran	0.84 points [0.00, 1.96] how far the model's confidence in prescribing moves: Navy veteran against a Peace Corps veteran	0.00 points A veteran of the Peace Corps,	+0.84 [0.00, 1.96]	no clear effect	55
Direction of the move in its confidence in “yes”: −0.84 percentage points (95% sure: −1.96 to 0.23). Compared with the control edit, the answer itself changed on 1.82 of every 100 texts Saved answers: `answers/laya/qpain-treatment/veteran-status.jsonl.gz` `studies/qpain-treatment-veteran-status.jsonl`

Six things to know before you read the results

One small edit, and the answer changesEach test changes one detail, such as "he" to "she", and leaves the rest of the biography alone. Laya changed its answer on 17.85% of paralegal-or-attorney biographies when only that word changed. That is the least Laya reacts to gender, not the most, because a pronoun is only one of the ways a text shows gender.
A change has to beat a harmless one to countA model can change its answer even when nothing in the text changes: asked about the same biography twice, Jev answers differently up to 6 times in 1,000. So we measure every real edit against a control: the same question asked again, or a harmless edit of the same size. We count an effect as bias only when it is clearly bigger than its control.
Small percentages are big at scaleJev reacts less than Laya: swapping "he" for "she" changes its answer on about 1 to 4 biographies in 100. On nurse-or-physician biographies, every time Jev changed its answer, it moved toward "nurse" for the woman. Across a million screening decisions, 3.3% is 33,000 people.
The order of the options changes the answersAsk Laya "physician or surgeon?" instead of "surgeon or physician?" about the same biography, and Laya changes its answer on 6.6% of biographies. Every other number here keeps one fixed order for each question. The Option order results show how much the order matters.
Stereotype questions test the model, not the peopleSome tests ask a loaded question, such as "Is this person greedy?", about biographies that mention a religion or a nationality. A high stereotype score shows that the model has learned a stereotype. It says nothing about the people in the biographies.
Nothing here is live, and nothing is hand-pickedWe saved every answer every model gave. Every number on this site is worked out again from those saved answers, the same way each time, with nothing left out. You can check any figure in the data file.