<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="delecraz-etal-2022-transparency">
    <titleInfo>
        <title>Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sebastien</namePart>
        <namePart type="family">Delecraz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Loukman</namePart>
        <namePart type="family">Eltarr</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Olivier</namePart>
        <namePart type="family">Oullier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ingo</namePart>
            <namePart type="family">Siegert</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mickael</namePart>
            <namePart type="family">Rigault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Victoria</namePart>
            <namePart type="family">Arranz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We introduce how the proprietary machine learning algorithms developed by Gojob, an HR Tech company, to match candidates to a job offer are as transparent and explainable as possible to users (i.e., our recruiters) and our clients (e.g. companies looking to fill jobs). We detail how our matching algorithm (which identifies the best candidates for a job offer) controls the fairness of its outcome. We have described the steps we have taken to ensure that the decisions made by our mathematical models not only inform but improve the performance of our recruiters.</abstract>
    <identifier type="citekey">delecraz-etal-2022-transparency</identifier>
    <location>
        <url>https://aclanthology.org/2022.legal-1.7</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>38</start>
            <end>43</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management
%A Delecraz, Sebastien
%A Eltarr, Loukman
%A Oullier, Olivier
%Y Siegert, Ingo
%Y Rigault, Mickael
%Y Arranz, Victoria
%S Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F delecraz-etal-2022-transparency
%X We introduce how the proprietary machine learning algorithms developed by Gojob, an HR Tech company, to match candidates to a job offer are as transparent and explainable as possible to users (i.e., our recruiters) and our clients (e.g. companies looking to fill jobs). We detail how our matching algorithm (which identifies the best candidates for a job offer) controls the fairness of its outcome. We have described the steps we have taken to ensure that the decisions made by our mathematical models not only inform but improve the performance of our recruiters.
%U https://aclanthology.org/2022.legal-1.7
%P 38-43

Markdown (Informal)

[Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management](https://aclanthology.org/2022.legal-1.7) (Delecraz et al., LEGAL 2022)

• Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management (Delecraz et al., LEGAL 2022)

ACL

• Sebastien Delecraz, Loukman Eltarr, and Olivier Oullier. 2022. Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management. In Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference, pages 38–43, Marseille, France. European Language Resources Association.