Videoconference interviews are now integral to many selection processes. Theoretical arguments and empirical findings suggest that videoconference interviews may lead to different interview performance ratings in comparison to Face-to-Face (FTF) interviews. This has led to the question of the comparability of the psychometric properties of videoconferences and FTF interviews. However, evidence from actual selection processes stems from the beginning of the century, and recent findings predominantly stem from simulated interview contexts. We present insights from an actual selection process within a large European organization where we had the unique opportunity for a quasi-experimental investigation of differences between videoconference and FTF interviews. Initially, the organization conducted FTF interviews, and after the onset of the COVID-19 pandemic, the interviews were conducted via videoconference. We examine mean differences in applicant performance ratings and evidence for response format-related validity differences. There were only small, non-significant mean differences and no evidence for response format related validity differences. We discuss possible causes for discrepancies in our findings compared to previous research. Furthermore, we conclude that downstream consequences of differences between FTF and videoconference interviews may be lower than previously expected. We end with a call for research on the interaction between technology-design and selection-tool-design features.@en

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.@en

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.

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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.@en