The Impact of Name Age Perception on Job Recommendations in LLM s (2025)

Abstract: Names often carry generational connotations, with certain names stereotypically associated with younger or older age groups. This study examines implicit age-related name bias in LLMs used for job recommendations. Analyzing six LLMs and 117 American names categorized by perceived age across 30 occupations, we find systematic bias: older-sounding names are favored for senior roles, while younger-sounding names are linked to youth-dominant jobs, reinforcing generational stereotypes. We also find that this bias is based on perceived rather than real ages associated with the names.

## introduction & related work llms have become increasingly integrated into hiring tools @xcite , chatbots @xcite , and recommendation systems @xcite , intensifying concerns about fairness and bias @xcite . in particular, their growing use in workforce recruitment and human resource management has drawn attention, as they offer the potential to optimize tasks like resume screening and candidate assessment @xcite @xcite , yet also risk reinforcing hiring biases @xcite . llms exhibit biases related to name @xcite , age (harris, 2023), gender @xcite , race @xcite , and disability @xcite in job recommendations and hiring decisions. while previous studies have examined how demographic factors like name, age, gender, and race influence job recommendations, no research has specifically investigated implicit age-related name bias in llms. one potential source of bias is the association of certain names with specific age groups, where "generational names" may inadvertently trigger stereotypes in ai-driven systems, leading to implicit age discrimination. this paper explores the potential bias of llms in associating names stereotypically linked to specific age groups with certain job roles. we investigate whether llms recommend candidates differently for roles based on name-age stereotypes. specifically, we examine whether names typically associated with younger individuals (e.g., 'cayden' or @xcite are more likely to be assigned to positions where the workforce skews younger, while names typically associated with older generations (e.g., @xcite or @xcite are recommended for roles where employees are generally older. in this research paper, we address three pivotal research questions. rq1: how does the perceived age associated with a name affect llm hiring recommendations for different job positions? rq2: are the effects of name-based age perception on llm hiring recommendations grounded in real age differences? rq3: what are the associations and assumptions regarding generational names and job roles that drive llm recommendations? ## name and occupation collection a list of american names was curated from johfre's (2020) study, along with corresponding age ranges. the perceived age group names are derived from participants' perceptions of age associated with a name, as collected through amazon mechanical turk (mturk). the perceived age group contains 117 names. the names are categorized into two groups: ## results and discussion desired llms behavior. an ideal language model should demonstrate fairness and impartiality in job recommendations, ensuring that candidate selection is based solely on relevant qualifications rather than implicit biases associated with names. for this study, this means that when evaluating candidates, llms should exhibit no discernible pattern of favoring younger-or older-name candidates for youth-or senior-dominant positions. ## conclusion our study reveals that llms exhibit strong agerelated name biases, reinforcing stereotypes in job recommendations. older-sounding names are disproportionately assigned to senior roles, while younger names are preferred for entry-level positions, despite identical qualifications. this bias poses risks in ai-driven hiring, necessitating mitigation strategies such as bias-aware training, fairness constraints, and human oversight. addressing these issues is essential to prevent discrimination and promote equitable hiring practices.