How different backgrounds in video interviews can bias evaluations of applicants
Bibliographic record
Abstract
Abstract Organizations are increasingly using technology‐enabled formats such as asynchronous video interviews (AVIs) to evaluate candidates. However, the personal environment of applicants visible in AVI recordings may introduce additional bias in the evaluation of interview performance. This study extends existing research by examining the influence of cues signaling affiliation with Islam or homosexuality in the background and comparing them with a neutral background using an experimental design and a German sample ( N = 222). Results showed that visible signs of religious affiliation with Islam led to lower perceived competence, while perceived warmth and interview performance were unaffected. Visual cues of homosexuality had no effect on perceptions of the applicant. In addition, personal characteristics of the raters, such as their intrinsic religious orientation or their attitudes towards homosexuality influenced applicants’ ratings, so that a non‐Muslim religious orientation was negatively associated with evaluations of the Muslim candidate and a negative attitude towards homosexuality was negatively associated with evaluations of the homosexual candidate. This study thus contributes to the literature on AVIs and discrimination against Muslims and members of the 2SLGBTQI+ community in personnel selection contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".