Counterfactual audit of LLM résumé scoring

A fair evaluator scores a résumé on its merits. Change only a demographic signal on it, the candidate's name, country, alma mater, or a gap in employment, and leave every other word untouched, and the score should not move. The optimistic assumption we put to the test is that frontier LLMs already behave this way: that they are effectively identity-blind, and swapping a name leaves the verdict intact.

A counterfactual audit: hold the résumé, job and model fixed, change one demographic line, and watch the score.

Step 1. Take one real résumé as the baseline and generate variants that each differ from it by a single demographic line. For example, the baseline candidate becomes Maria Rodriguez for a first-name swap, or the address country becomes India, with every other word left identical.

Step 2. Score the baseline and every variant with the same model on the same job, several times each. For example, all 11 models score the 30 résumé variants across 17 jobs, repeated over multiple runs.

Step 3. For each variant measure the delta, its mean score minus the baseline's, and check whether that gap clears the run to run noise. For example, a model that scores the baseline 6.2 and the India variant 5.6 has dropped the score by 0.6.

Step 4. Pool the deltas two ways: by model, to see which is least even-handed, and by dimension, to see which swapped signal moves scores the most. The two tables below are exactly those two views.

Step 5. Read it. If demographic swaps leave the score flat, the model is identity-blind and the assumption holds. If the score moves, and moves the same way across runs and across models, the evaluator is not judging the work alone.

When the only change is United States (axis: Company Locations), Gemini 2.5 Flash shifts its score by -3.40 on the role: Junior / Mid-Level Fullstack Developer.

See this counterfactual →

Each row is one model. We measure how far that model's score moves, on average, when we swap a single demographic signal on the résumé. The higher the number, the less even-handed the model. "Most penalised" and "most rewarded" call out the single variant that swung scores furthest in each direction.

Same data, grouped by what we changed instead of who did the changing. The mean |Δ| pools every model, variant, and job for each demographic axis. The axis at the top is the one models react to most reliably.

The identity-blind assumption does not hold, but the failure is messier than "the models are biased". Scores do move when only a demographic line changes: the most sensitive model, Qwen 3 Next 80B, shifts its score by 0.405 per swap on average, and the signal that moves scores the most is First Name at 0.261. No model here is truly identity-blind.

What keeps this short of clean discrimination: only about 4% of individual score shifts clear the run to run noise floor, and the models barely agree on direction (mean pairwise correlation +0.07, near zero). That points less at a stable, shared prejudice and more at each model being an unstable judge whose number wobbles when any part of the input changes, which is its own reason not to hand it a hiring decision unsupervised. The methodology covers the noise floor, and the heatmap the model-by-model agreement.