  Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit
applicability across numerous tasks. One domain of interest is their use in
algorithmic hiring, specifically in matching resumes with job categories. Yet,
this introduces issues of bias on protected attributes like gender, race and
maternity status. The seminal work of Bertrand & Mullainathan (2003) set the
gold-standard for identifying hiring bias via field experiments where the
response rate for identical resumes that differ only in protected attributes,
e.g., racially suggestive names such as Emily or Lakisha, is compared. We
replicate this experiment on state-of-art LLMs (GPT-3.5, Bard, Claude and
Llama) to evaluate bias (or lack thereof) on gender, race, maternity status,
pregnancy status, and political affiliation. We evaluate LLMs on two tasks: (1)
matching resumes to job categories; and (2) summarizing resumes with employment
relevant information. Overall, LLMs are robust across race and gender. They
differ in their performance on pregnancy status and political affiliation. We
use contrastive input decoding on open-source LLMs to uncover potential sources
of bias.

Computation and Language;Artificial Intelligence;Machine Learning