How does bias enter the employment interview? Identifying the riskiest applicant characteristics, interviewer characteristics, and sources of potentially biasing information
Bibliographic record
Abstract
Abstract The current study examines the riskiest forms and sources of potential bias in the employment interview. A mixed methods survey focused on interviewers' attention to various potentially biasing applicant characteristics, how interviewers learn about such characteristics, the traits of interviewers who are sensitive to such characteristics, and how knowledge of such characteristics affects interviewers' opinions of applicants. Participants were 680 professional interviewers from the U.S., U.K., and Canada. High risks of bias were associated with six applicant characteristics (as targets of bias), three interviewer characteristics (as predisposing toward bias), and three sources of information in the interview process. Interviewers commonly rationalized their judgments in job‐relevant terms. These results have implications for understanding and limiting the risk of common selection biases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".