Please use this identifier to cite or link to this item: doi:10.22028/D291-31248
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Title: Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
Author(s): Liem, Cynthia C. S.
Langer, Markus
Demetriou, Andrew
Hiemstra, Annemarie M. F.
Sukma Wicaksana, Achmadnoer
Born, Marise Ph.
König, Cornelius J.
Editor(s): Escalante, Hugo Jair
Escalera, Sergio
Guyon, Isabelle
Baró, Xavier
Güçlütürk, Yağmur
Güçlü, Umut
Gerven, Marcel van
Language: English
Title: Explainable and Interpretable Models in Computer Vision and Machine Learning
Startpage: 197
Endpage: 253
Publisher/Platform: Springer
Year of Publication: 2018
Place of publication: Cham
Publikation type: Book Chapter
Abstract: In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered.
DOI of the first publication: 10.1007/978-3-319-98131-4_9
URL of the first publication:
Link to this record: hdl:20.500.11880/29289
ISBN: 978-3-319-98130-7
Date of registration: 19-Jun-2020
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Psychologie
Professorship: HW - Prof. Dr. Cornelius König
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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