Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening

Cynthia C.S. Liem, Markus Langer, Andrew Demetriou, Annemarie M.F. Hiemstra, Sukma Achmadnoer Sukma Wicaksana, Marise Ph. Born, Cornelis J. König

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

886 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationExplainable and Interpretable Models in Computer Vision and Machine Learning
EditorsH. Jair Escalante, S. Escalera, I. Guyon, X. Baró, Y Güçlütürk
Place of PublicationCham
Number of pages57
ISBN (Electronic)978-3-319-98131-4
ISBN (Print)978-3-319-98130-7
Publication statusPublished - 2018

Publication series

NameThe Springer Series on Challenges in Machine Learning
ISSN (Print)2520-131X
ISSN (Electronic)2520-1328

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Psychology
  • Machine learning
  • Job candidate screening
  • Methodology
  • Explainability
  • Multimodal analysis
  • Interdisciplinarity


Dive into the research topics of 'Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening'. Together they form a unique fingerprint.

Cite this