De (On)mogelijkheden van machine learning voor het verminderen van bias en discriminatie bij personeelsbeslissingen

Translated title of the contribution: The promises and perils of machine learning algorithms to reduce bias and discrimination in personnel selection procedures

Annemarie M.F. Hiemstra*, Tatjana Cassel, Marise Ph Born, Cynthia C.S. Liem

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

Abstract

In this article, we describe the implementation of algorithms based on machine learning for personnel selection procedures and how this data-driven approach corresponds to and differentiates from classical psychological assessment. We discuss if, and in what way, bias and discrimination occur when using algorithms based on machine learning for personnel selection. For this reason, we conducted a literature review (covering 2016-2019) from which 41 articles were included. The results indicate that algorithms possibly lead to reduced (indirect) discrimination compared to some other selection methods. This is one of the reasons why the development of algorithms for personnel selection has increased quickly and the number of vendors has grown fast. It is insufficiently possible yet, however, to ascertain if the promise is kept. First, this is because algorithms are often trade secrets (lack of transparency). Second, the validity and reliability of data used for the development of algorithms are not always clear. Furthermore, psychological selection issues about diversity and validity cannot (yet) be solved by algorithms. The increasing attention for the topic, expressed by a large growth in publications, is hopeful. We conclude with recommendations for the detection and reduction of bias and discrimination when using machine learning algorithms for personnel selection.

Translated title of the contributionThe promises and perils of machine learning algorithms to reduce bias and discrimination in personnel selection procedures
Original languageDutch
Pages (from-to)279-299
Number of pages21
JournalGedrag en Organisatie
Volume33
Issue number4
DOIs
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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.

Keywords

  • Algorithms
  • Bias
  • Discrimination
  • Machine learning
  • Personnel selection

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