Bias in the background? The role of background information in asynchronous video interviews
Bibliographic record
Abstract
Summary Asynchronous video interviews (AVIs) have become popular tools for applicant selection. Although AVIs are standardized, extant research remains silent on whether this novel interview format could introduce new forms of bias. Because many applicants complete AVIs from their homes, their video background could provide evaluators with information about stigmatizing features that (a) are usually “invisible” in traditional selection contexts but become observable in AVIs, (b) are not always legally protected, and (c) can impact evaluators' judgments. Across three experimental studies, we examined how cues indicating parental status (Study 1), sexual orientation (Study 2), and political affiliation (Study 3) can impact perceptions of applicant warmth and competence and ratings of interview performance and potential work performance. The effect of background information varied by stigmatized feature. Applicants depicted as parents were perceived to be higher on warmth and received higher interview performance ratings but were not evaluated more negatively on competence or potential work performance. There was no effect of sexual orientation on any outcome variables. However, applicants who supported the same political party as the evaluator were viewed as warmer and received higher ratings of interview performance and potential work performance. Thus, organizations should encourage applicants to use neutral backgrounds.
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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 | 0.036 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".