A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting

Renate Burema; Anne Schuth; Christopher Spelt; Dong Nguyen

doi:10.63317/3gdjhdj7otjm

A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting

Renate Burema, Anne Schuth, Christopher Spelt, Dong Nguyen

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the external publication .)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

In this paper, we present a Dutch benchmark to assess whether large language models (LLMs) exhibit social biases in hiring decisions, focusing on gender and country of origin. We experiment with two approaches: explicit descriptions of the applicants’ demographics and using first names as proxies. We evaluate both monolingual and multilingual LLMs and find that all tested models, gpt-4o-mini, claude-3.5-haiku, Geitje-7B-Ultra and EuroLLM-9B-Instruct, exhibit some degree of social bias in their decisions. Furthermore, all models tested are sensitive to the manner in which the prompts are written. We make our benchmark publicly available under an EUPL-1.2 license. The benchmark is available at https://github.com/MinBZK/llm-benchmark/tree/main/benchmarks/social-bias.

Anthology ID:: 2026.lrec-1.312
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 3932–3943
Language:
External URL:: https://lrec.elra.info/lrec2026-main-312
DOI:: 10.63317/3gdjhdj7otjm
Bibkey:
Cite (ACL):: Renate Burema, Anne Schuth, Christopher Spelt, and Dong Nguyen. 2026. A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 3932–3943, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting (Burema et al., LREC 2026)
Copy Citation:

External Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{burema-etal-2026-dutch,
    title = "A {D}utch Benchmark to Assess Social Bias in {LLM}s within a Hiring Decision Setting",
    author = "Burema, Renate  and
      Schuth, Anne  and
      Spelt, Christopher  and
      Nguyen, Dong",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.312/",
    doi = "10.63317/3gdjhdj7otjm",
    pages = "3932--3943",
    abstract = "In this paper, we present a Dutch benchmark to assess whether large language models (LLMs) exhibit social biases in hiring decisions, focusing on gender and country of origin. We experiment with two approaches: explicit descriptions of the applicants' demographics and using first names as proxies. We evaluate both monolingual and multilingual LLMs and find that all tested models, gpt-4o-mini, claude-3.5-haiku, Geitje-7B-Ultra and EuroLLM-9B-Instruct, exhibit some degree of social bias in their decisions. Furthermore, all models tested are sensitive to the manner in which the prompts are written. We make our benchmark publicly available under an EUPL-1.2 license. The benchmark is available at \url{https://github.com/MinBZK/llm-benchmark/tree/main/benchmarks/social-bias}."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="burema-etal-2026-dutch">
    <titleInfo>
        <title>A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Renate</namePart>
        <namePart type="family">Burema</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anne</namePart>
        <namePart type="family">Schuth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="family">Spelt</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dong</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we present a Dutch benchmark to assess whether large language models (LLMs) exhibit social biases in hiring decisions, focusing on gender and country of origin. We experiment with two approaches: explicit descriptions of the applicants’ demographics and using first names as proxies. We evaluate both monolingual and multilingual LLMs and find that all tested models, gpt-4o-mini, claude-3.5-haiku, Geitje-7B-Ultra and EuroLLM-9B-Instruct, exhibit some degree of social bias in their decisions. Furthermore, all models tested are sensitive to the manner in which the prompts are written. We make our benchmark publicly available under an EUPL-1.2 license. The benchmark is available at https://github.com/MinBZK/llm-benchmark/tree/main/benchmarks/social-bias.</abstract>
    <identifier type="citekey">burema-etal-2026-dutch</identifier>
    <identifier type="doi">10.63317/3gdjhdj7otjm</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.312/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>3932</start>
            <end>3943</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting
%A Burema, Renate
%A Schuth, Anne
%A Spelt, Christopher
%A Nguyen, Dong
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F burema-etal-2026-dutch
%X In this paper, we present a Dutch benchmark to assess whether large language models (LLMs) exhibit social biases in hiring decisions, focusing on gender and country of origin. We experiment with two approaches: explicit descriptions of the applicants’ demographics and using first names as proxies. We evaluate both monolingual and multilingual LLMs and find that all tested models, gpt-4o-mini, claude-3.5-haiku, Geitje-7B-Ultra and EuroLLM-9B-Instruct, exhibit some degree of social bias in their decisions. Furthermore, all models tested are sensitive to the manner in which the prompts are written. We make our benchmark publicly available under an EUPL-1.2 license. The benchmark is available at https://github.com/MinBZK/llm-benchmark/tree/main/benchmarks/social-bias.
%R 10.63317/3gdjhdj7otjm
%U https://aclanthology.org/2026.lrec-1.312/
%U https://doi.org/10.63317/3gdjhdj7otjm
%P 3932-3943

Download as File

Markdown (Informal)

[A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting](https://aclanthology.org/2026.lrec-1.312/) (Burema et al., LREC 2026)

A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting (Burema et al., LREC 2026)

ACL

Renate Burema, Anne Schuth, Christopher Spelt, and Dong Nguyen. 2026. A Dutch Benchmark to Assess Social Bias in LLMs within a Hiring Decision Setting. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 3932–3943, Palma de Mallorca, Spain. ELRA Language Resource Association.