Implicit bias reduction that lasts: Putting Situational Attribution Training to the test
Bibliographic record
Abstract
Abstract Addressing the damaging effects of implicit stereotypes—spontaneous, awareness‐independent associations between social groups and particular traits—remains a social imperative. These biases have been linked to negative outcomes in settings ranging from the workplace to medical care facilities. However, many techniques found to reduce implicit biases have been shown to yield short‐lived effects. In the present experiment, we assessed the longevity of reduced implicit racial stereotyping resulting from an intensive training technique that focuses on weakening the fundamental attributional processes underlying implicit stereotyping. Specifically, we aimed to strengthen the likelihood of White participants to consider situational attributions for behaviors performed by Black men that might otherwise have been judged to reflect negative African American stereotypes. White participants were randomly assigned to complete either Situational Attribution Training (SAT), a technique comprised of intensive training (480 trials) to “consider the situation” when making judgments about stereotype‐consistent behaviors performed by Black men, or a control task. Implicit stereotyping was assessed 24h later via the Person Categorization Task and found to be reduced for SAT, versus control, participants even after this delay. Implications for future antibias research and practice are considered.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".