Discrimination due to Ethnicity and Gender: How susceptible are video‐based job interviews?

Kroll, Esther; Ziegler, Matthias

doi:10.1111/ijsa.12138

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…This alone can be seen as a significant fairness advantage over traditional interviewing techniques. Personal bias toward interviewees can be further reduced when combined with computational approaches [28] and video-based communication [29]. Virtual reality and other digital environments have been successfully deployed for training purposes, particularly for effective interview preparation.…”

Section: Discussionmentioning

confidence: 99%

A System That Allows Users to Have a Job Interview Experience

Vardarlıer

2023

Sustainability

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Today, corporate companies spend a lot of time on and attach importance to job interviews in recruitment. In job interviews, they select or eliminate many of the candidates using interview techniques. This poses a serious problem, especially for candidates who have not had job interview experience, even though they meet sufficient requirements. Because these candidates do not have previous interview experience, they experience more difficulties in this process. In this regard, the study is about a system created in a virtual environment that allows a job interview experience through a user interface. The system subject of the study is a model consisting of a user interface unit, server, interview information database, language processing unit, behavior analysis unit, virtual interview unit, reporting unit, and social media integration unit. This proposed model has been detailed modularly through the web-based system.

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Section: Discussionmentioning

confidence: 99%

A System That Allows Users to Have a Job Interview Experience

Vardarlıer

2023

Sustainability

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“…However, literature has shown that factors such as gender, ethnicity, physical appearance or obesity do have an influence, even though taking a decision on these factors is expressly forbidden by law in both Europe and the United States [6]. Moreover, in the context of AVIs, having access to the image of the candidates early in the selection process has a potential impact on increasing the influence of aesthetics [7], gender or ethnicity of the candidates [8]. Approaches to mitigate discrimination in job interviews may differ from country to country [9].…”

Section: A Discrimination In Job Interviewsmentioning

confidence: 99%

Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews

Hemamou¹,

Guillon²,

Martin³

et al. 2021

Preprint

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Use of machine learning for automatic analysis of job interview videos has recently seen increased interest. Despite claims of fair output regarding sensitive information such as gender or ethnicity of the candidates, the current approaches rarely provide proof of unbiased decision-making, or that sensitive information is not used. Recently, adversarial methods have been proved to effectively remove sensitive information from the latent representation of neural networks. However, these methods rely on the use of explicitly labeled protected variables (e.g. gender), which cannot be collected in the context of recruiting in some countries (e.g. France). In this article, we propose a new adversarial approach to remove sensitive information from the latent representation of neural networks without the need to collect any sensitive variable. Using only a few frames of the interview, we train our model to not be able to find the face of the candidate related to the job interview in the inner layers of the model. This, in turn, allows us to remove relevant private information from these layers. Comparing our approach to a standard baseline on a public dataset with gender and ethnicity annotations, we show that it effectively removes sensitive information from the main network. Moreover, to the best of our knowledge, this is the first application of adversarial techniques for obtaining a multimodal fair representation in the context of video job interviews. In summary, our contributions aim at improving fairness of the upcoming automatic systems processing videos of job interviews for equality in job selection.

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“…Dimensions such as anxiety, self-efficacy, its predictive validity, performance outcome, and the impact of age, accent, age, and gender, were previously investigated [10,11]. Moreover, it was shown that anchoring-and-adjuments heuristics and associated motivational mechanisms drive bias against stigmatized people in interview decisions [12].…”

Section: Introductionmentioning

confidence: 99%

“We Will Let You Know”: An Assessment of Digital vs. Face-to-Face Job Interviews via EEG Connectivity Analysis

2022

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We focused on job interviews as critical examples of complex social interaction in organizational contexts. We aimed at investigating the effect of face-to-face vs. computer-mediated interaction, of role (candidate, recruiter), and of the interview phase (introductory, attitudinal, technical, conclusive) on intra-brain and inter-brain connectivity measures and autonomic synchronization. Twenty expert recruiters and potential candidates took part in a hyperscanning investigation. Namely, electroencephalography (delta, theta, alpha, beta bands) and autonomic (skin-conductance, heart-rate) data were collected in candidate-recruiter dyads during a simulated job interview and then concurrently analyzed. Analyses highlighted a link between face-to-face condition and greater intra-/inter-brain connectivity indices in delta and theta bands. Furthermore, intra-brain and inter-brain connectivity measures were higher for delta and theta bands in the final interview phases compared to the first ones. Consistently, autonomic synchronization was higher during the final interview phases, specifically in the face-to-face condition. Finally, recruiters showed higher intra-brain connectivity in the delta range over frontal and temporoparietal areas, while candidates showed higher intra-brain connectivity in the theta range over frontal areas. Findings highlight the value of hyperscanning investigations in exploring social attunement in professional contexts and hint at their potential to foster neuroscience-informed practices in human resource management processes.

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Discrimination due to Ethnicity and Gender: How susceptible are video‐based job interviews?

Cited by 12 publications

References 48 publications

A System That Allows Users to Have a Job Interview Experience

A System That Allows Users to Have a Job Interview Experience

Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews

“We Will Let You Know”: An Assessment of Digital vs. Face-to-Face Job Interviews via EEG Connectivity Analysis

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