Facing one’s implicit biases: From awareness to acknowledgment.
Bibliographic record
Abstract
Expanding on conflicting theoretical conceptualizations of implicit bias, 6 studies tested the effectiveness of different procedures to increase acknowledgment of harboring biases against minorities. Participants who predicted their responses toward pictures of various minority groups on future implicit association tests (IATs) showed increased alignment between implicit and explicit preferences (Studies 1-3), greater levels of explicit bias (Studies 1-3), and increased self-reported acknowledgment of being racially biased (Studies 4-6). In all studies, effects of IAT score prediction were significant even when participants did not actually complete IATs. Effects of predicting IAT scores were moderated by nonprejudicial goals, in that IAT score prediction increased acknowledgment of bias for participants with strong nonprejudicial goals, but not for participants with weak nonprejudicial goals (Study 4). Mere completion of IATs and feedback on IAT performance had inconsistent effects across studies and criterion measures. Instructions to attend to one's spontaneous affective reactions toward minority group members increased acknowledgment of bias to the same extent as IAT score prediction (Study 6). The findings are consistent with conceptualizations suggesting that (a) implicit evaluations are consciously experienced as spontaneous affective reactions and (b) directing people's attention to their spontaneous affective reactions can increase acknowledgment of bias. Implications for theoretical conceptualizations of implicit bias and interventions that aim to reduce discrimination via increased acknowledgment of bias are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".