Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/R\'esum\'e Evaluations

TL;DR

This study investigates gender and positional biases in LLM-based candidate evaluations, revealing consistent favoritism towards female-named candidates and positional preferences, raising concerns about fairness in automated hiring decisions.

Contribution

It provides empirical evidence of gender and positional biases in 22 LLMs during CV evaluation tasks, highlighting potential fairness issues in automated hiring systems.

Findings

LLMs favor female-named candidates despite identical qualifications

Adding explicit gender fields increases female candidate preference

Models show positional bias towards first-listed candidates

Abstract

This study examines the behavior of Large Language Models (LLMs) when evaluating professional candidates based on their resumes or curricula vitae (CVs). In an experiment involving 22 leading LLMs, each model was systematically given one job description along with a pair of profession-matched CVs, one bearing a male first name, the other a female first name, and asked to select the more suitable candidate for the job. Each CV pair was presented twice, with names swapped to ensure that any observed preferences in candidate selection stemmed from gendered names cues. Despite identical professional qualifications across genders, all LLMs consistently favored female-named candidates across 70 different professions. Adding an explicit gender field (male/female) to the CVs further increased the preference for female applicants. When gendered names were replaced with gender-neutral identifiers "Candidate A" and "Candidate B", several models displayed a preference to select "Candidate A". Counterbalancing gender assignment between these gender-neutral identifiers resulted in gender parity in candidate selection. When asked to rate CVs in isolation rather than compare pairs, LLMs assigned slightly higher average scores to female CVs overall, but the effect size was negligible. Including preferred pronouns (he/him or she/her) next to a candidate's name slightly increased the odds of the candidate being selected regardless of gender. Finally, most models exhibited a substantial positional bias to select the candidate listed first in the prompt. These findings underscore the need for caution when deploying LLMs in high-stakes autonomous decision-making contexts and raise doubts about whether LLMs consistently apply principled reasoning.