Why Anti-Bias Interventions (Need Not) Fail
Bibliographic record
Abstract
This manuscript was accepted for publication in Perspectives on Psychological Science on September 26, 2021. There is a critical disconnect between scientific knowledge about the nature of bias and how this knowledge gets translated into organizational de-biasing efforts. Conceptual confusion around what implicit bias is contributes to misunderstanding. Bridging these gaps is the key to understanding when and why anti-bias interventions will succeed or fail. Notably, there are multiple distinct pathways to biased behavior, each of which require different types of interventions. To bridge the gap between public understanding and psychological research, we introduce a visual typology of bias that summarizes the process by which group-relevant cognitions are expressed as biased behavior. Our typology spotlights cognitive, motivational, and situational variables impacting the expression and inhibition of biases while aiming to reduce the ambiguity of what constitutes implicit bias. We also address how norms modulate how biases unfold and are perceived by targets. Using this typology as a framework, we identify theoretically distinct entry points for anti-bias interventions. A key insight is that changing associations, increasing motivation, raising awareness, and changing norms are distinct goals, which require different types of interventions targeting individual, interpersonal, and institutional structures. We close with recommendations for anti-bias training grounded in the science of prejudice and stereotyping.
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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 | 0.030 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".