The behavior of LLMs in hiring decisions: Systemic biases in candidate selection

Previous studies have explored gender and ethnic biases in hiring by submitting résumés/CVs to real job postings or mock selection panels, systematically varying the gender or ethnicity signaled by applicants. This approach enables researchers to isolate the effects of demographic characteristics on hiring or preselection decisions. Building on this methodology, the present analysis evaluates whether Large Language Models (LLMs) exhibit algorithmic gender bias when tasked with selecting the most qualified candidate for a given job description. LLMs gender preferences in hiring In an experiment involving 22 leading LLMs and 70 popular professions, each model was systematically given a job description along with a pair of profession-matched CVs (one including a male first name, and 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 selectio ... Read full article.