@inproceedings{kamruzzaman-kim-2025-impact,
title = "The Impact of Name Age Perception on Job Recommendations in {LLM}s",
author = "Kamruzzaman, Mahammed and
Kim, Gene Louis",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.778/",
doi = "10.18653/v1/2025.findings-acl.778",
pages = "15033--15058",
ISBN = "979-8-89176-256-5",
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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kamruzzaman-kim-2025-impact">
<titleInfo>
<title>The Impact of Name Age Perception on Job Recommendations in LLMs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mahammed</namePart>
<namePart type="family">Kamruzzaman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gene</namePart>
<namePart type="given">Louis</namePart>
<namePart type="family">Kim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Nabende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Taher</namePart>
<namePart type="family">Pilehvar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-256-5</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">kamruzzaman-kim-2025-impact</identifier>
<identifier type="doi">10.18653/v1/2025.findings-acl.778</identifier>
<location>
<url>https://aclanthology.org/2025.findings-acl.778/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>15033</start>
<end>15058</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The Impact of Name Age Perception on Job Recommendations in LLMs
%A Kamruzzaman, Mahammed
%A Kim, Gene Louis
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F kamruzzaman-kim-2025-impact
%X 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.
%R 10.18653/v1/2025.findings-acl.778
%U https://aclanthology.org/2025.findings-acl.778/
%U https://doi.org/10.18653/v1/2025.findings-acl.778
%P 15033-15058
Markdown (Informal)
[The Impact of Name Age Perception on Job Recommendations in LLMs](https://aclanthology.org/2025.findings-acl.778/) (Kamruzzaman & Kim, Findings 2025)
ACL