AI Alignment Amplifies the Role of Race, Gender, and Disability in Hiring Decisions

Humans increasingly delegate consequential decisions to language models, yet whether these systems reproduce or reshape human patterns of discrimination remains unclear.

Here, across 29 models and 177 occupations covering nearly half of U.S. employment, we show that language models incorporate demographics into hiring decisions, advantaging female and Black candidates while penalising disabled candidates, with effect sizes comparable to six months to one year of additional education.

While pre-trained models show small demographic effects, post-training alignment, which adapts models to human norms and preferences, amplifies advantages for female and Black candidates by 396% and 413% and worsens the disability penalty by 152%.

Compared with human employers in past correspondence experiments, language models reverse racial discrimination, substantially attenuate the disability penalty, and amplify the female advantage.

Investigating the mechanisms, we find behavioural patterns that parallel statistical discrimination in human labour markets, but disability consistently fares worst across all channels.

We trace this asymmetry to the composition of alignment data, where disability is structurally underrepresented, and to models' internal representations, where alignment shifts the encoding of disability most negatively among the three marginalised groups.