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 ...