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

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

doi:10.1007/978-3-319-98131-4_9

Cited by 56 publications

(43 citation statements)

References 88 publications

(113 reference statements)

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“…This would help to achieve generalizability with previous results obtained with instructed participants and experimental/manipulated designs, with the aim of including behavioural features for faking detection in personnel and forensic real-life settings 55 . Moreover, future studies could focus on improving converging validity by applying additional behavioural and implicit parameters and measuring these with eye-tracking 65 and face-reading techniques 66 .…”

Section: Effect Of Time Pressure On Mouse Movements and Trajectoriesmentioning

confidence: 99%

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

Mazza

Monaro

Burla

et al. 2020

Sci Rep

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the aim of the present study was to explore whether kinematic indicators could improve the detection of subjects demonstrating faking-good behaviour when responding to personality questionnaires. one hundred and twenty volunteers were randomly assigned to one of four experimental groups (honest unspeeded, faking-good unspeeded, honest speeded, and faking-good speeded). Participants were asked to respond to the MMPI-2 underreporting scales (L, K, S) and the PPI-R Virtuous Responding (VR) scale using a computer mouse. The collected data included T-point scores on the L, K, S, and VR scales; response times on these scales; and several temporal and spatial mouse parameters. these data were used to investigate the presence of significant differences between the two manipulated variables (honest vs. faking-good; speeded vs. unspeeded). The results demonstrated that T-scores were significantly higher in the faking-good condition relative to the honest condition; however, fakinggood and honest respondents showed no statistically significant differences between the speeded and unspeeded conditions. Concerning temporal and spatial kinematic parameters, we observed mixed results for different scales and further investigations are required. The most consistent finding, albeit with small observed effects, regards the L scale, in which faking-good respondents took longer to respond to stimuli and outlined wider mouse trajectories to arrive at the given response.One of the main limitations of the use of self-report questionnaires to assess personality is that such tests are vulnerable to faking behavior 1 -that is, the tendency to deliberately distort one's responses in order to fulfil personal goals 2 . In one form of faking, respondents exaggerate or create symptoms to emphasize their psychological suffering and discomfort (faking-bad); in another, respondents present themselves in a particularly favourable fashion, emphasizing their desirable traits and rejecting their undesirable ones (faking-good). Faking behaviour is widespread in many contexts, with alarming estimates of prevalence (e.g., 30-50% in personnel selection 3 and up to 30% in forensic settings 4,5 ). Many studies have focused on faking-bad behaviour and developed tools to facilitate its detection; such tools include the Structured Interview of Reported Symptoms-2 (SIRS-2) 6 , the Structured Inventory of Malingered Symptomatology (SIMS) 7 , and the Inventory of Problems-29 8 . Faking-bad behaviour has received more research attention 9,10 perhaps because its welfare/social costs (in terms of, e.g. insurance compensation) are more easily recognizable; thus, the literature on the subject is not as rich and instruments to identify faking-good behaviour are lacking; for this reason, the present study focused on faking-good behavior, specifically.Analysis of validity scales is one of the most commonly used methods to detect fakers. Validity scales were designed to gather information on the validity and interpretability of self-report questionnaires by exploring the ...

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Section: Effect Of Time Pressure On Mouse Movements and Trajectoriesmentioning

confidence: 99%

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

Mazza

Monaro

Burla

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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“…Other studies indicate video interviews, through nonverbal visual human behavior analysis, are starting to modify the way in which applicants get hired [16]. Nevertheless, these kind of applications will only be truly accepted and trusted if explainability and transparency can be guaranteed [17]. Moreover, to become inclusive and benefit everyone, such systems need to be able to generalize to different contexts.…”

Section: The Importance Of First Impressions In Our Livesmentioning

confidence: 99%

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Jacques

Güçlütürk

Pérez

et al. 2022

IEEE Trans. Affective Comput.

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Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.

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“…They can narrow down their screening by eliminating candidates who are highly nervous and sensitive, i.e., those having high values of neuroticism trait. [60] discusses the job candidate screening problem from an interdisciplinary viewpoint of psychologists and machine learning scientists.…”

Section: Applicationsmentioning

confidence: 99%

Recent trends in deep learning based personality detection

et al. 2019

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Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.

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

Cited by 56 publications

References 88 publications

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis

Recent trends in deep learning based personality detection

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