Algorithm Audit: LLMs Tilt Doctor Recommendations by Demographics Worth $7-$14 a Visit, and Mention It in 0.03% of Explanations

arXiv 2608.14399·medium signalXBlueskyLinkedInCopy link

A prespecified randomized audit ran seven models (six open-weight plus gpt-4o-mini) over 3,024 choice sets, three personas, nine paraphrases and nine arms for 40,068 scored responses picking among synthetic physician cards. Reputation dominates — a 3.9→4.7 rating raises choice probability 31.4 points, a $90→$190 fee lowers it 20.0 — but demographic parity is rejected in the opposite direction from human audit studies: female-signaled names gain 2.5 points and Hispanic-, South-Asian- and Black-signaled names gain 1.3-2.9 over White-signaled, plus $11 of value in merely being listed first. The governance-relevant finding is that models named gender or ethnicity in at most 0.03% of their stated reasons, so transparency rules built on model self-report would miss every one of these effects.