Assessing biasing factors in asynchronous video interviews: applicant completion decisions, video background, and evaluation format
Bibliographic record
Abstract
Asynchronous video interviews (AVIs) have become popular selection methods due to their flexibility and cost savings but might introduce new forms of bias. For instance, interviewees often complete them from home, their surroundings might signal personal or protected statuses, and technology issues might distort the information provided. This paper leverages two complementary studies to examine (a) the AVI completion decisions, recording quality, and background elements present in high- and low-stakes job interviews, (b) to what extent these AVI-specific elements and interviewees’ characteristics can bias performance ratings, and (c) whether evaluation standardization can help mitigate such biases. Study 1 used mock interviews with (N = 626) Prolific participants evaluated by professional hiring managers. Study 2 involved high-stakes interviews with (N = 523) real applicants for competitive education programmes evaluated by trained raters using either standardized or unstandardized approaches. AVI elements (attire, room tidiness, technical issues, background) were coded in both studies. Results showed that completion decisions depended on AVI stakes and could influence evaluations. Issues with recording quality were rare and modestly related to AVI evaluations. AVI backgrounds signalling personal or protected statuses were very rare and unrelated to evaluations. Evaluations standardization reduced bias only in relation to sex-based differences, but not other interviewee characteristics.
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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 | 0.147 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".